1
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Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Birman D, Manavi S, Chen S, Windolf C, Hurwitz C, Namima T, Pedraja F, Weiss S, Raducanu B, Ness TV, Jia X, Mastroberardino G, Rossi LF, Carandini M, Häusser M, Einevoll GT, Laurent G, Sawtell NB, Bair W, Pasupathy A, Lopez CM, Dutta B, Paninski L, Siegle JH, Koch C, Olsen SR, Harris TD, Steinmetz NA. Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings. bioRxiv 2024:2023.08.23.554527. [PMID: 37662298 PMCID: PMC10473688 DOI: 10.1101/2023.08.23.554527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.
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Affiliation(s)
- Zhiwen Ye
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Andrew M. Shelton
- MindScope Program, Allen Institute, Seattle, WA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Jordan R. Shaker
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Julien Boussard
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Daniel Birman
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Sahar Manavi
- MindScope Program, Allen Institute, Seattle, WA, USA
| | - Susu Chen
- Janelia Research Campus, Ashburn, VA, USA
| | - Charlie Windolf
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Cole Hurwitz
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Tomoyuki Namima
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Federico Pedraja
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Shahaf Weiss
- Max Planck Institute for Brain Research, Frankfurt, Germany
| | | | | | - Xiaoxuan Jia
- Center for Life Sciences & IDG/McGovern Institute for Brain Research, Tsinghua University, China
| | - Giulia Mastroberardino
- UCL Institute of Ophthalmology, University College London, London, UK
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - L. Federico Rossi
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Gaute T. Einevoll
- Norwegian University of Life Sciences, Ås, Norway
- University of Oslo, Oslo, Norway
| | - Gilles Laurent
- Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Nathaniel B. Sawtell
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Wyeth Bair
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Anitha Pasupathy
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | | | | | - Liam Paninski
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Christof Koch
- MindScope Program, Allen Institute, Seattle, WA, USA
| | - Shawn R. Olsen
- MindScope Program, Allen Institute, Seattle, WA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Timothy D. Harris
- Janelia Research Campus, Ashburn, VA, USA
- Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD, USA
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2
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Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner M, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, Cunningham JP, Sawtell NB, Paninski L. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. bioRxiv 2024:2023.04.28.538703. [PMID: 37162966 PMCID: PMC10168383 DOI: 10.1101/2023.04.28.538703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Anup Khanal
- University of California Los Angeles, Los Angeles, USA
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3
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Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. bioRxiv 2024:2024.02.05.578988. [PMID: 38370650 PMCID: PMC10871230 DOI: 10.1101/2024.02.05.578988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
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Affiliation(s)
- Andrew J Zimnik
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - K Cora Ames
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Xinyue An
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA
| | - Laura Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, CA, USA
| | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Vladislav Susoy
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Joshua I Glaser
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
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Windolf C, Yu H, Paulk AC, Meszéna D, Muñoz W, Boussard J, Hardstone R, Caprara I, Jamali M, Kfir Y, Xu D, Chung JE, Sellers KK, Ye Z, Shaker J, Lebedeva A, Raghavan M, Trautmann E, Melin M, Couto J, Garcia S, Coughlin B, Horváth C, Fiáth R, Ulbert I, Movshon JA, Shadlen MN, Churchland MM, Churchland AK, Steinmetz NA, Chang EF, Schweitzer JS, Williams ZM, Cash SS, Paninski L, Varol E. DREDge: robust motion correction for high-density extracellular recordings across species. bioRxiv 2023:2023.10.24.563768. [PMID: 37961359 PMCID: PMC10634799 DOI: 10.1101/2023.10.24.563768] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.
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Affiliation(s)
- Charlie Windolf
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Han Yu
- Zuckerman Institute, Columbia University
- Department of Electrical Engineering, Columbia University
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Domokos Meszéna
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - William Muñoz
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Julien Boussard
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Richard Hardstone
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Irene Caprara
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Mohsen Jamali
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Yoav Kfir
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Duo Xu
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jason E Chung
- Department of Neurological Surgery, University of California San Francisco
| | - Kristin K Sellers
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Zhiwen Ye
- Department of Biological Structure, University of Washington
| | - Jordan Shaker
- Department of Biological Structure, University of Washington
| | | | | | - Eric Trautmann
- Department of Neuroscience, Columbia University Medical Center
- Zuckerman Institute, Columbia University
- Grossman Center for the Statistics of Mind, Columbia University
| | - Max Melin
- David Geffen School of Medicine, University of California Los Angeles
| | - João Couto
- David Geffen School of Medicine, University of California Los Angeles
| | - Samuel Garcia
- Centre National de la Recherche Scientifique, Centre de Recherche en Neurosciences de Lyon
| | - Brian Coughlin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Csaba Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | | | - Michael N Shadlen
- Zuckerman Institute, Columbia University
- Howard Hughes Medical Institute
| | | | - Anne K Churchland
- David Geffen School of Medicine, University of California Los Angeles
| | | | - Edward F Chang
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jeffrey S Schweitzer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Liam Paninski
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Neuroscience, Columbia University Medical Center
- Grossman Center for the Statistics of Mind, Columbia University
| | - Erdem Varol
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Computer Science & Engineering, New York University
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5
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Triplett MA, Gajowa M, Adesnik H, Paninski L. Bayesian target optimisation for high-precision holographic optogenetics. bioRxiv 2023:2023.05.25.542307. [PMID: 37292661 PMCID: PMC10246014 DOI: 10.1101/2023.05.25.542307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.
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Affiliation(s)
- Marcus A. Triplett
- Department of Statistics, Columbia University
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Marta Gajowa
- Department of Molecular and Cell Biology, UC Berkeley
| | | | - Liam Paninski
- Department of Statistics, Columbia University
- Zuckerman Mind Brain Behavior Institute, Columbia University
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6
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Zhang Y, He T, Boussard J, Windolf C, Winter O, Trautmann E, Roth N, Barrell H, Churchland M, Steinmetz NA, Varol E, Hurwitz C, Paninski L. Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. bioRxiv 2023:2023.09.21.558869. [PMID: 37790422 PMCID: PMC10542538 DOI: 10.1101/2023.09.21.558869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.
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7
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Pasarkar A, Kinsella I, Zhou P, Wu M, Pan D, Fan JL, Wang Z, Abdeladim L, Peterka DS, Adesnik H, Ji N, Paninski L. maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. bioRxiv 2023:2023.09.14.557777. [PMID: 37745388 PMCID: PMC10515957 DOI: 10.1101/2023.09.14.557777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells' shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.
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Affiliation(s)
- Amol Pasarkar
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Ian Kinsella
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
| | - Pengcheng Zhou
- Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China
| | - Melissa Wu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Daisong Pan
- Department of Physics, University of California, Berkeley, California 94720, USA
| | - Jiang Lan Fan
- Joint Bioengineering Graduate Program, University of California, Berkeley, CA 94720
| | - Zhen Wang
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, CA, 90095, USA
| | - Lamiae Abdeladim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Darcy S Peterka
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Liam Paninski
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
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8
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Schaffer ES, Mishra N, Whiteway MR, Li W, Vancura MB, Freedman J, Patel KB, Voleti V, Paninski L, Hillman EMC, Abbott LF, Axel R. The spatial and temporal structure of neural activity across the fly brain. Nat Commun 2023; 14:5572. [PMID: 37696814 PMCID: PMC10495430 DOI: 10.1038/s41467-023-41261-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 08/29/2023] [Indexed: 09/13/2023] Open
Abstract
What are the spatial and temporal scales of brainwide neuronal activity? We used swept, confocally-aligned planar excitation (SCAPE) microscopy to image all cells in a large volume of the brain of adult Drosophila with high spatiotemporal resolution while flies engaged in a variety of spontaneous behaviors. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons correlated (or anticorrelated) with running and flailing over timescales that ranged from seconds to a minute. Grooming elicited a weaker global response. Significant residual activity not directly correlated with behavior was high dimensional and reflected the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate.
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Affiliation(s)
- Evan S Schaffer
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA.
| | - Neeli Mishra
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Matthew R Whiteway
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Statistics and the Grossman Center for the Statistics of Mind, Columbia University, New York, NY, 10027, USA
| | - Wenze Li
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Michelle B Vancura
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Jason Freedman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Kripa B Patel
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Venkatakaushik Voleti
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Statistics and the Grossman Center for the Statistics of Mind, Columbia University, New York, NY, 10027, USA
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
- Department of Radiology, Columbia University, New York, NY, 10027, USA
| | - L F Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, 10032, USA
| | - Richard Axel
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, 10032, USA
- Howard Hughes Medical Institute, Columbia University, New York, NY, 10027, USA
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9
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Chen S, Rao BY, Herrlinger S, Losonczy A, Paninski L, Varol E. MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM. Proc IEEE Int Conf Acoust Speech Signal Process 2023; 2023:10.1109/icassp49357.2023.10096185. [PMID: 37388235 PMCID: PMC10308861 DOI: 10.1109/icassp49357.2023.10096185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.
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Affiliation(s)
- Shuonan Chen
- Department of System Biology
- Zuckerman Institute
- Columbia University
| | - Bovey Y Rao
- Department of Neurobiology
- Zuckerman Institute
- Columbia University
| | | | - Attila Losonczy
- Department of Neurobiology
- Zuckerman Institute
- Columbia University
| | - Liam Paninski
- Department of Statistics
- Zuckerman Institute
- Columbia University
| | - Erdem Varol
- Department of Statistics
- Department of Computer Science & Engineering
- Zuckerman Institute
- Columbia University
- New York University
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10
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Nejatbakhsh A, Dey N, Venkatachalam V, Yemini E, Paninski L, Varol E. Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. Inf Process Med Imaging 2023; 13939:332-343. [PMID: 37476079 PMCID: PMC10358289 DOI: 10.1007/978-3-031-34048-2_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.
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Affiliation(s)
- Amin Nejatbakhsh
- Departments of Neuroscience and Statistics, Columbia University, New York, USA
| | - Neel Dey
- Computer Science and Artificial Intelligence Lab, MIT, Massachusetts, USA
| | | | - Eviatar Yemini
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, USA
| | - Liam Paninski
- Departments of Neuroscience and Statistics, Columbia University, New York, USA
| | - Erdem Varol
- Department of Computer Science and Engineering, New York University, New York, USA
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11
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Windolf C, Paulk AC, Kfir Y, Trautmann E, Meszéna D, Muñoz W, Caprara I, Jamali M, Boussard J, Williams ZM, Cash SS, Paninski L, Varol E. ROBUST ONLINE MULTIBAND DRIFT ESTIMATION IN ELECTROPHYSIOLOGY DATA. Proc IEEE Int Conf Acoust Speech Signal Process 2023; 2023:10.1109/icassp49357.2023.10095487. [PMID: 37388234 PMCID: PMC10308877 DOI: 10.1109/icassp49357.2023.10095487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.
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Affiliation(s)
- Charlie Windolf
- Department of Statistics
- Zuckerman Institute
- Columbia University
| | - Angelique C Paulk
- Department of Neurology
- Center for Neurotechnology and Neurorecovery
- Massachusetts General Hospital
- Harvard Medical School
| | - Yoav Kfir
- Department of Neurosurgery
- Massachusetts General Hospital
- Harvard Medical School
| | | | - Domokos Meszéna
- Department of Neurology
- Center for Neurotechnology and Neurorecovery
- Massachusetts General Hospital
- Harvard Medical School
| | - William Muñoz
- Department of Neurosurgery
- Massachusetts General Hospital
- Harvard Medical School
| | - Irene Caprara
- Department of Neurosurgery
- Massachusetts General Hospital
- Harvard Medical School
| | - Mohsen Jamali
- Department of Neurosurgery
- Massachusetts General Hospital
- Harvard Medical School
| | - Julien Boussard
- Department of Statistics
- Zuckerman Institute
- Columbia University
| | - Ziv M Williams
- Department of Neurosurgery
- Massachusetts General Hospital
- Harvard Medical School
| | - Sydney S Cash
- Department of Neurology
- Center for Neurotechnology and Neurorecovery
- Massachusetts General Hospital
- Harvard Medical School
| | - Liam Paninski
- Department of Statistics
- Zuckerman Institute
- Columbia University
| | - Erdem Varol
- Department of Statistics
- Department of Computer Science & Engineering
- Zuckerman Institute
- Columbia University
- New York University
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12
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Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, Cunningham JP. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron 2022; 110:2771-2789.e7. [PMID: 35870448 PMCID: PMC9464703 DOI: 10.1016/j.neuron.2022.06.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.
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Affiliation(s)
- Taiga Abe
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32607, USA
| | - E Kelly Buchanan
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Joao Couto
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - John Briggs
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sian Lee Kitt
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Ryan Glassman
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - John Zhou
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - John P Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA.
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13
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Chen S, Loper J, Zhou P, Paninski L. Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. PLoS Comput Biol 2022; 18:e1009991. [PMID: 35395020 PMCID: PMC9020678 DOI: 10.1371/journal.pcbi.1009991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 04/20/2022] [Accepted: 03/07/2022] [Indexed: 11/19/2022] Open
Abstract
Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.
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Affiliation(s)
- Shuonan Chen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Jackson Loper
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
| | - Pengcheng Zhou
- Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
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14
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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15
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Tekieli T, Yemini E, Nejatbakhsh A, Wang C, Varol E, Fernandez RW, Masoudi N, Paninski L, Hobert O. Visualizing the organization and differentiation of the male-specific nervous system of C. elegans. Development 2021; 148:271902. [PMID: 34415309 DOI: 10.1242/dev.199687] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/16/2021] [Indexed: 01/08/2023]
Abstract
Sex differences in the brain are prevalent throughout the animal kingdom and particularly well appreciated in the nematode Caenorhabditis elegans, where male animals contain a little-studied set of 93 male-specific neurons. To make these neurons amenable for future study, we describe here how a multicolor reporter transgene, NeuroPAL, is capable of visualizing the distinct identities of all male-specific neurons. We used NeuroPAL to visualize and characterize a number of features of the male-specific nervous system. We provide several proofs of concept for using NeuroPAL to identify the sites of expression of gfp-tagged reporter genes and for cellular fate analysis by analyzing the effect of removal of several developmental patterning genes on neuronal identity acquisition. We use NeuroPAL and its intrinsic cohort of more than 40 distinct differentiation markers to show that, even though male-specific neurons are generated throughout all four larval stages, they execute their terminal differentiation program in a coordinated manner in the fourth larval stage. This coordinated wave of differentiation, which we call 'just-in-time' differentiation, couples neuronal maturation programs with the appearance of sexual organs.
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Affiliation(s)
- Tessa Tekieli
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Amin Nejatbakhsh
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Chen Wang
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Erdem Varol
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Robert W Fernandez
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Neda Masoudi
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Oliver Hobert
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
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16
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Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel JP, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, Cunningham JP, Paninski L. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput Biol 2021; 17:e1009439. [PMID: 34550974 PMCID: PMC8489729 DOI: 10.1371/journal.pcbi.1009439] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 10/04/2021] [Accepted: 09/09/2021] [Indexed: 12/02/2022] Open
Abstract
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.
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Affiliation(s)
- Matthew R. Whiteway
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Dan Biderman
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Yoni Friedman
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America
| | - Mario Dipoppa
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - E. Kelly Buchanan
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Anqi Wu
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - John Zhou
- Department of Computer Science, Columbia University, New York, New York, United States of America
| | | | - Nathaniel J. Miska
- Sainsbury-Wellcome Centre for Neural Circuits and Behavior, University College London, London, United Kingdom
| | - Jean-Paul Noel
- Center for Neural Science, New York University, New York, New York, United States of America
| | - Erica Rodriguez
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | | | - Karolina Socha
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Anne E. Urai
- Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | - C. Daniel Salzman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Psychiatry, Columbia University, New York, New York, United States of America
- New York State Psychiatric Institute, New York, New York, United States of America
- Kavli Institute for Brain Sciences, New York, New York, United States of America
| | | | - John P. Cunningham
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
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17
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Rao BY, Peterson AM, Kandror EK, Herrlinger S, Losonczy A, Paninski L, Rizvi AH, Varol E. Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. Med Image Comput Comput Assist Interv 2021; 12908:466-475. [PMID: 35274110 PMCID: PMC8905828 DOI: 10.1007/978-3-030-87237-3_45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.
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Affiliation(s)
- Bovey Y Rao
- Department of Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Alexis M Peterson
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Elena K Kandror
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Stephanie Herrlinger
- Department of Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of the Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Abbas H Rizvi
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Erdem Varol
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of the Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
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18
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Couto J, Musall S, Sun XR, Khanal A, Gluf S, Saxena S, Kinsella I, Abe T, Cunningham JP, Paninski L, Churchland AK. Chronic, cortex-wide imaging of specific cell populations during behavior. Nat Protoc 2021; 16:3241-3263. [PMID: 34075229 PMCID: PMC8788140 DOI: 10.1038/s41596-021-00527-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 02/26/2021] [Indexed: 02/04/2023]
Abstract
Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Wide-field imaging of genetically encoded indicators is a high-throughput, cost-effective and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a wide-field macroscope setup, performing surgery to prepare the intact skull and imaging neural activity chronically in behaving, transgenic mice. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets laboratories that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging and/or analyze cortex-wide neuronal recordings. The entire protocol, including steps for assembly and calibration of the macroscope, surgical preparation, imaging and data analysis, requires a total of 8 h. It is designed to be accessible to laboratories with limited expertise in imaging methods or interest in high-throughput imaging during behavior.
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Affiliation(s)
- Joao Couto
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Simon Musall
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich, Jülich, Germany
- Department of Neurophysiology, Institute of Biology 2, RWTH Aachen University, Aachen, Germany
| | - Xiaonan R Sun
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
- Department of Neurosurgery, Zucker School of Medicine, Hofstra University, Hempstead, NY, USA
| | - Anup Khanal
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Steven Gluf
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
| | - Ian Kinsella
- Department of Statistics, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Taiga Abe
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - John P Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Anne K Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA.
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19
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Kim YJ, Brackbill N, Batty E, Lee J, Mitelut C, Tong W, Chichilnisky EJ, Paninski L. Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Comput 2021; 33:1719-1750. [PMID: 34411268 DOI: 10.1162/neco_a_01395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/25/2021] [Indexed: 11/04/2022]
Abstract
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.
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20
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Xie ME, Adam Y, Fan LZ, Böhm UL, Kinsella I, Zhou D, Rozsa M, Singh A, Svoboda K, Paninski L, Cohen AE. High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo. Cell Rep 2021; 35:108954. [PMID: 33826882 PMCID: PMC8095336 DOI: 10.1016/j.celrep.2021.108954] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/24/2021] [Accepted: 03/15/2021] [Indexed: 11/24/2022] Open
Abstract
The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.
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Affiliation(s)
- Michael E Xie
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Yoav Adam
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Linlin Z Fan
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Urs L Böhm
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ian Kinsella
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Ding Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Marton Rozsa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Amrita Singh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA.
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
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21
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Chen S, Loper J, Chen X, Vaughan A, Zador AM, Paninski L. BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Comput Biol 2021; 17:e1008256. [PMID: 33684106 PMCID: PMC7971881 DOI: 10.1371/journal.pcbi.1008256] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 03/18/2021] [Accepted: 02/13/2021] [Indexed: 11/22/2022] Open
Abstract
Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the 'NeuroCAAS' cloud platform.
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Affiliation(s)
- Shuonan Chen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Jackson Loper
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
| | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Alex Vaughan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Anthony M. Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
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22
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Yemini E, Lin A, Nejatbakhsh A, Varol E, Sun R, Mena GE, Samuel ADT, Paninski L, Venkatachalam V, Hobert O. NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans. Cell 2021; 184:272-288.e11. [PMID: 33378642 PMCID: PMC10494711 DOI: 10.1016/j.cell.2020.12.012] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 06/30/2020] [Accepted: 12/08/2020] [Indexed: 12/31/2022]
Abstract
Comprehensively resolving neuronal identities in whole-brain images is a major challenge. We achieve this in C. elegans by engineering a multicolor transgene called NeuroPAL (a neuronal polychromatic atlas of landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that resolves all neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. We showcase three applications that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding for these stimuli.
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Affiliation(s)
- Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA.
| | - Albert Lin
- Department of Physics, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Amin Nejatbakhsh
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Erdem Varol
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Ruoxi Sun
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Gonzalo E Mena
- Department of Statistics and Data Science Initiative, Harvard University, Cambridge, MA 02138, USA
| | - Aravinthan D T Samuel
- Department of Physics, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | | | - Oliver Hobert
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
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23
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Lacefield CO, Pnevmatikakis EA, Paninski L, Bruno RM. Reinforcement Learning Recruits Somata and Apical Dendrites across Layers of Primary Sensory Cortex. Cell Rep 2020; 26:2000-2008.e2. [PMID: 30784583 PMCID: PMC7001879 DOI: 10.1016/j.celrep.2019.01.093] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 09/27/2018] [Accepted: 01/24/2019] [Indexed: 01/20/2023] Open
Abstract
The mammalian brain can form associations between behaviorally relevant stimuli in an animal’s environment. While such learning is thought to primarily involve high-order association cortex, even primary sensory areas receive long-range connections carrying information that could contribute to high-level representations. Here, we imaged layer 1 apical dendrites in the barrel cortex of mice performing a whisker-based operant behavior. In addition to sensory-motor events, calcium signals in apical dendrites of layers 2/3 and 5 neurons and in layer 2/3 somata track the delivery of rewards, both choice related and randomly administered. Reward-related tuft-wide dendritic spikes emerge gradually with training and are task specific. Learning recruits cells whose intrinsic activity coincides with the time of reinforcement. Layer 4 largely lacked reward-related signals, suggesting a source other than the primary thalamus. Our results demonstrate that a sensory cortex can acquire a set of associations outside its immediate sensory modality and linked to salient behavioral events. Previously, the only known triggers of apical dendritic spikes were “bottom-up”events, such as appropriate sensory stimuli or an animal’s location in space. Lacefield et al. show that reinforced associations are powerful triggers of apical dendrite activity and that reward can manipulate perceptions at their earliest stages of cortical processing.
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Affiliation(s)
- Clay O Lacefield
- Department of Neuroscience, Mortimer Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | | | - Liam Paninski
- Department of Neuroscience, Mortimer Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Randy M Bruno
- Department of Neuroscience, Mortimer Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
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24
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Saxena S, Kinsella I, Musall S, Kim SH, Meszaros J, Thibodeaux DN, Kim C, Cunningham J, Hillman EMC, Churchland A, Paninski L. Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput Biol 2020; 16:e1007791. [PMID: 32282806 PMCID: PMC7179949 DOI: 10.1371/journal.pcbi.1007791] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 04/23/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022] Open
Abstract
Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.
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Affiliation(s)
- Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Simon Musall
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Sharon H Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Jozsef Meszaros
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - David N Thibodeaux
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Carla Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - John Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Anne Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
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25
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Wei X, Zhu R, Paninski L. A new method to analyze the variations of neural tuning and its application to primate V1. J Vis 2019. [DOI: 10.1167/19.10.271b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Xuexin Wei
- Center for Theoretical Neuroscience
- Department of Statistics
- Columbia University
| | - Rong Zhu
- Center for Theoretical Neuroscience
- Department of Statistics
- Columbia University
| | - Liam Paninski
- Center for Theoretical Neuroscience
- Department of Statistics
- Columbia University
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26
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Abdelfattah AS, Kawashima T, Singh A, Novak O, Liu H, Shuai Y, Huang YC, Campagnola L, Seeman SC, Yu J, Zheng J, Grimm JB, Patel R, Friedrich J, Mensh BD, Paninski L, Macklin JJ, Murphy GJ, Podgorski K, Lin BJ, Chen TW, Turner GC, Liu Z, Koyama M, Svoboda K, Ahrens MB, Lavis LD, Schreiter ER. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 2019; 365:699-704. [PMID: 31371562 DOI: 10.1126/science.aav6416] [Citation(s) in RCA: 228] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 07/17/2019] [Indexed: 11/30/2023]
Abstract
Genetically encoded voltage indicators (GEVIs) enable monitoring of neuronal activity at high spatial and temporal resolution. However, the utility of existing GEVIs has been limited by the brightness and photostability of fluorescent proteins and rhodopsins. We engineered a GEVI, called Voltron, that uses bright and photostable synthetic dyes instead of protein-based fluorophores, thereby extending the number of neurons imaged simultaneously in vivo by a factor of 10 and enabling imaging for significantly longer durations relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In the mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously over a 15-minute period of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.
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Affiliation(s)
- Ahmed S Abdelfattah
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Takashi Kawashima
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Amrita Singh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ondrej Novak
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Department of Auditory Neuroscience, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - Hui Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Yi-Chieh Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei 112, Taiwan
| | | | | | - Jianing Yu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Jihong Zheng
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Jonathan B Grimm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ronak Patel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Johannes Friedrich
- Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
- Department of Neuroscience and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Brett D Mensh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Liam Paninski
- Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
- Department of Neuroscience and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - John J Macklin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kaspar Podgorski
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Bei-Jung Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei 112, Taiwan
| | - Tsai-Wen Chen
- Institute of Neuroscience, National Yang-Ming University, Taipei 112, Taiwan
| | - Glenn C Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Zhe Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Minoru Koyama
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Luke D Lavis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Eric R Schreiter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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27
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Naka A, Veit J, Shababo B, Chance RK, Risso D, Stafford D, Snyder B, Egladyous A, Chu D, Sridharan S, Mossing DP, Paninski L, Ngai J, Adesnik H. Complementary networks of cortical somatostatin interneurons enforce layer specific control. eLife 2019; 8:43696. [PMID: 30883329 PMCID: PMC6422636 DOI: 10.7554/elife.43696] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 02/08/2019] [Indexed: 12/03/2022] Open
Abstract
The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.
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Affiliation(s)
- Alexander Naka
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Julia Veit
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Ben Shababo
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Rebecca K Chance
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Davide Risso
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,Department of Statistical Sciences, University of Padova, Padova, Italy.,Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, United States
| | - David Stafford
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Benjamin Snyder
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Andrew Egladyous
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Desiree Chu
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Savitha Sridharan
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Daniel P Mossing
- Department of Biophysics, University of California, Berkeley, Berkeley, United States
| | - Liam Paninski
- Neurobiology and Behavior Program, Columbia University, New York, United States.,Center for Theoretical Neuroscience, Columbia University, New York, United States.,Departments of Statistics and Neuroscience, Columbia University, New York, United States.,Grossman Center for the Statistics of Mind, Columbia University, New York, United States
| | - John Ngai
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,QB3 Functional Genomics Laboratory, University of California, Berkeley, Berkeley, United States
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
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28
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Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr Opin Neurobiol 2019; 50:232-241. [PMID: 29738986 DOI: 10.1016/j.conb.2018.04.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 03/12/2018] [Accepted: 04/06/2018] [Indexed: 01/01/2023]
Abstract
Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
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Affiliation(s)
- L Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States; Department of Neuroscience, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States.
| | - J P Cunningham
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States
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29
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Berens P, Freeman J, Deneux T, Chenkov N, McColgan T, Speiser A, Macke JH, Turaga SC, Mineault P, Rupprecht P, Gerhard S, Friedrich RW, Friedrich J, Paninski L, Pachitariu M, Harris KD, Bolte B, Machado TA, Ringach D, Stone J, Rogerson LE, Sofroniew NJ, Reimer J, Froudarakis E, Euler T, Román Rosón M, Theis L, Tolias AS, Bethge M. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput Biol 2018; 14:e1006157. [PMID: 29782491 PMCID: PMC5997358 DOI: 10.1371/journal.pcbi.1006157] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 06/12/2018] [Accepted: 04/24/2018] [Indexed: 11/25/2022] Open
Abstract
In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.
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Affiliation(s)
- Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
| | - Jeremy Freeman
- Chan Zuckerberg Initiative, San Francisco, California, United States of America
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Thomas Deneux
- Unit of Neuroscience Information and Complexity, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Nikolay Chenkov
- Bernstein Center for Computational Neuroscience and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thomas McColgan
- Bernstein Center for Computational Neuroscience and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Artur Speiser
- Research Center Caesar, an associate of the Max Planck Society, Bonn, Germany
| | - Jakob H. Macke
- Research Center Caesar, an associate of the Max Planck Society, Bonn, Germany
- Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
| | - Srinivas C. Turaga
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Patrick Mineault
- Independent Researcher, San Francisco, California, United States of America
| | - Peter Rupprecht
- Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Stephan Gerhard
- Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland
| | - Rainer W. Friedrich
- Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Johannes Friedrich
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Marius Pachitariu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- Institute of Neurology, University College, London, United Kingdom
| | | | - Ben Bolte
- Departments of Mathematics and Computer Science, Emory University, Atlanta, United States of America
| | - Timothy A. Machado
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Dario Ringach
- Neurobiology and Psychology, Jules Stein Eye Institute, Biomedical Engineering Program, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Jasmine Stone
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- Departement of Computer Science, Yale University, New Haven, Connecticut, United States of America
| | - Luke E. Rogerson
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
| | - Nicolas J. Sofroniew
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
| | - Thomas Euler
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
| | - Miroslav Román Rosón
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Division of Neurobiology, Department Biology II, LMU Munich, Munich, Germany
| | | | - Andreas S. Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, United States of America
| | - Matthias Bethge
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
- Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
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30
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Zhou P, Resendez SL, Rodriguez-Romaguera J, Jimenez JC, Neufeld SQ, Giovannucci A, Friedrich J, Pnevmatikakis EA, Stuber GD, Hen R, Kheirbek MA, Sabatini BL, Kass RE, Paninski L. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 2018; 7:e28728. [PMID: 29469809 PMCID: PMC5871355 DOI: 10.7554/elife.28728] [Citation(s) in RCA: 321] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 02/20/2018] [Indexed: 12/12/2022] Open
Abstract
In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.
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Affiliation(s)
- Pengcheng Zhou
- Center for the Neural Basis of CognitionCarnegie Mellon UniversityPittsburghUnited States
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUnited States
- Grossman Center for the Statistics of MindColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
| | - Shanna L Resendez
- Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUnited States
| | | | - Jessica C Jimenez
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Division of Integrative Neuroscience, Department of PsychiatryNew York State Psychiatric InstituteNew YorkUnited States
- Department of Psychiatry & PharmacologyColumbia UniversityNew YorkUnited States
| | - Shay Q Neufeld
- Department of NeurobiologyHarvard Medical School, Howard Hughes Medical InstituteBostonUnited States
| | - Andrea Giovannucci
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | - Johannes Friedrich
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | | | - Garret D Stuber
- Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and PhysiologyUniversity of North Carolina at Chapel HillChapel HillUnited States
- Neuroscience CenterUniversity of North Carolina at Chapel HillChapel HillUnited States
| | - Rene Hen
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Division of Integrative Neuroscience, Department of PsychiatryNew York State Psychiatric InstituteNew YorkUnited States
- Department of Psychiatry & PharmacologyColumbia UniversityNew YorkUnited States
| | - Mazen A Kheirbek
- Weill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoUnited States
- Neuroscience Graduate ProgramUniversity of CaliforniaSan FranciscoUnited States
- Kavli Institute for Fundamental NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoUnited States
| | - Bernardo L Sabatini
- Department of NeurobiologyHarvard Medical School, Howard Hughes Medical InstituteBostonUnited States
| | - Robert E Kass
- Center for the Neural Basis of CognitionCarnegie Mellon UniversityPittsburghUnited States
- Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUnited States
- Department of StatisticsCarnegie Mellon UniversityPittsburghUnited States
| | - Liam Paninski
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Grossman Center for the Statistics of MindColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Kavli Institute for Brain ScienceColumbia UniversityNew YorkUnited States
- Neurotechnology CenterColumbia UniversityNew YorkUnited States
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31
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Jimenez JC, Su K, Goldberg AR, Luna VM, Biane JS, Ordek G, Zhou P, Ong SK, Wright MA, Zweifel L, Paninski L, Hen R, Kheirbek MA. Anxiety Cells in a Hippocampal-Hypothalamic Circuit. Neuron 2018; 97:670-683.e6. [PMID: 29397273 PMCID: PMC5877404 DOI: 10.1016/j.neuron.2018.01.016] [Citation(s) in RCA: 314] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 12/04/2017] [Accepted: 01/05/2018] [Indexed: 11/25/2022]
Abstract
The hippocampus is traditionally thought to transmit contextual information to limbic structures where it acquires valence. Using freely moving calcium imaging and optogenetics, we show that while the dorsal CA1 subregion of the hippocampus is enriched in place cells, ventral CA1 (vCA1) is enriched in anxiety cells that are activated by anxiogenic environments and required for avoidance behavior. Imaging cells defined by their projection target revealed that anxiety cells were enriched in the vCA1 population projecting to the lateral hypothalamic area (LHA) but not to the basal amygdala (BA). Consistent with this selectivity, optogenetic activation of vCA1 terminals in LHA but not BA increased anxiety and avoidance, while activation of terminals in BA but not LHA impaired contextual fear memory. Thus, the hippocampus encodes not only neutral but also valence-related contextual information, and the vCA1-LHA pathway is a direct route by which the hippocampus can rapidly influence innate anxiety behavior.
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Affiliation(s)
- Jessica C Jimenez
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Katy Su
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Alexander R Goldberg
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Victor M Luna
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Jeremy S Biane
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Gokhan Ordek
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Pengcheng Zhou
- Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA; Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Kavli Institute for Brain Science, and NeuroTechnology Center, Columbia University, New York, NY, USA
| | - Samantha K Ong
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Matthew A Wright
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Larry Zweifel
- Department of Pharmacology, University of Washington, Seattle, WA 98105, USA; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98105, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Kavli Institute for Brain Science, and NeuroTechnology Center, Columbia University, New York, NY, USA
| | - René Hen
- Departments of Neuroscience, Psychiatry & Pharmacology, Columbia University, New York, NY, USA; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, NY, USA.
| | - Mazen A Kheirbek
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, USA; Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, USA.
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32
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Yu K, Ahrens S, Zhang X, Schiff H, Ramakrishnan C, Fenno L, Deisseroth K, Zhao F, Luo MH, Gong L, He M, Zhou P, Paninski L, Li B. The central amygdala controls learning in the lateral amygdala. Nat Neurosci 2017; 20:1680-1685. [PMID: 29184202 PMCID: PMC5755715 DOI: 10.1038/s41593-017-0009-9] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 09/29/2017] [Indexed: 01/28/2023]
Abstract
Experience-driven synaptic plasticity in the lateral amygdala is thought to underlie the formation of associations between sensory stimuli and an ensuing threat. However, how the central amygdala participates in such a learning process remains unclear. Here we show that PKC-δ-expressing central amygdala neurons are essential for the synaptic plasticity underlying learning in the lateral amygdala, as they convey information about the unconditioned stimulus to lateral amygdala neurons during fear conditioning.
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Affiliation(s)
- Kai Yu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Sandra Ahrens
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xian Zhang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Hillary Schiff
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Charu Ramakrishnan
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering and Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Lief Fenno
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering and Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering and Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Fei Zhao
- State Key Laboratory of Virology, CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China
| | - Min-Hua Luo
- State Key Laboratory of Virology, CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China
| | - Ling Gong
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Miao He
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Pengcheng Zhou
- Departments of Statistics and Neuroscience, Center for Theoretical Neuroscience, and Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Center for Theoretical Neuroscience, and Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Bo Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput Biol 2017; 13:e1005842. [PMID: 29131818 PMCID: PMC5703587 DOI: 10.1371/journal.pcbi.1005842] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.
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Affiliation(s)
- Gonzalo E. Mena
- Statistics Department, Columbia University, New York, New York, United States of America
| | - Lauren E. Grosberg
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Sasidhar Madugula
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Paweł Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland
| | - Alan Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - John Cunningham
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - E. J. Chichilnisky
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Liam Paninski
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
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Klaus A, Martins GJ, Paixao VB, Zhou P, Paninski L, Costa RM. The Spatiotemporal Organization of the Striatum Encodes Action Space. Neuron 2017; 95:1171-1180.e7. [PMID: 28858619 PMCID: PMC5584673 DOI: 10.1016/j.neuron.2017.08.015] [Citation(s) in RCA: 139] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Revised: 07/12/2017] [Accepted: 08/09/2017] [Indexed: 11/16/2022]
Abstract
Activity in striatal direct- and indirect-pathway spiny projection neurons (SPNs) is critical for proper movement. However, little is known about the spatiotemporal organization of this activity. We investigated the spatiotemporal organization of SPN ensemble activity in mice during self-paced, natural movements using microendoscopic imaging. Activity in both pathways showed predominantly local but also some long-range correlations. Using a novel approach to cluster and quantify behaviors based on continuous accelerometer and video data, we found that SPN ensembles active during specific actions were spatially closer and more correlated overall. Furthermore, similarity between different actions corresponded to the similarity between SPN ensemble patterns, irrespective of movement speed. Consistently, the accuracy of decoding behavior from SPN ensemble patterns was directly related to the dissimilarity between behavioral clusters. These results identify a predominantly local, but not spatially compact, organization of direct- and indirect-pathway SPN activity that maps action space independently of movement speed. Direct- and indirect-pathway SPNs show locally biased spatiotemporal organization SPNs active during a particular behavior are more correlated and spatially closer SPN ensembles encode action identity independently of movement speed Distance between behaviors corresponds to distance between SPN ensemble patterns
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Affiliation(s)
- Andreas Klaus
- Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon 1400-038, Portugal.
| | - Gabriela J Martins
- Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon 1400-038, Portugal
| | - Vitor B Paixao
- Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon 1400-038, Portugal
| | - Pengcheng Zhou
- Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Rui M Costa
- Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon 1400-038, Portugal; Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
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Friedrich J, Yang W, Soudry D, Mu Y, Ahrens MB, Yuste R, Peterka DS, Paninski L. Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Comput Biol 2017; 13:e1005685. [PMID: 28771570 PMCID: PMC5557609 DOI: 10.1371/journal.pcbi.1005685] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 08/15/2017] [Accepted: 07/14/2017] [Indexed: 11/19/2022] Open
Abstract
Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.
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Affiliation(s)
- Johannes Friedrich
- Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- * E-mail: (JF); (LP)
| | - Weijian Yang
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Daniel Soudry
- Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Yu Mu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Misha B. Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Rafael Yuste
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Kavli Institute of Brain Science, Columbia University, New York, New York, United States of America
| | - Darcy S. Peterka
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Kavli Institute of Brain Science, Columbia University, New York, New York, United States of America
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- * E-mail: (JF); (LP)
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Picardo MA, Merel J, Katlowitz KA, Vallentin D, Okobi DE, Benezra SE, Clary RC, Pnevmatikakis EA, Paninski L, Long MA. Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch. Neuron 2017; 90:866-76. [PMID: 27196976 DOI: 10.1016/j.neuron.2016.02.016] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Revised: 01/14/2016] [Accepted: 02/04/2016] [Indexed: 12/13/2022]
Abstract
The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise time points during song production. However, the neural representation of this behavior at a population level remains elusive. We used two-photon microscopy to monitor ensemble activity during singing, integrating across multiple trials by adopting a Bayesian inference approach to more precisely estimate burst timing. Additionally, we examined spiking and motor-related synaptic inputs using intracellular recordings during singing. With both experimental approaches, we find that premotor events do not occur preferentially at the onsets or offsets of song syllables or at specific subsyllabic motor landmarks. These results strongly support the notion that HVC projection neurons collectively exhibit a temporal sequence during singing that is uncoupled from ongoing movements.
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Affiliation(s)
- Michel A Picardo
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Josh Merel
- Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Kalman A Katlowitz
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Daniela Vallentin
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Daniel E Okobi
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Sam E Benezra
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Rachel C Clary
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Eftychios A Pnevmatikakis
- Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA
| | - Liam Paninski
- Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Michael A Long
- New York University Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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Giovannucci A, Badura A, Deverett B, Najafi F, Pereira TD, Gao Z, Ozden I, Kloth AD, Pnevmatikakis E, Paninski L, De Zeeuw CI, Medina JF, Wang SSH. Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nat Neurosci 2017; 20:727-734. [PMID: 28319608 DOI: 10.1038/nn.4531] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 02/17/2017] [Indexed: 12/19/2022]
Abstract
Cerebellar granule cells, which constitute half the brain's neurons, supply Purkinje cells with contextual information necessary for motor learning, but how they encode this information is unknown. Here we show, using two-photon microscopy to track neural activity over multiple days of cerebellum-dependent eyeblink conditioning in mice, that granule cell populations acquire a dense representation of the anticipatory eyelid movement. Initially, granule cells responded to neutral visual and somatosensory stimuli as well as periorbital airpuffs used for training. As learning progressed, two-thirds of monitored granule cells acquired a conditional response whose timing matched or preceded the learned eyelid movements. Granule cell activity covaried trial by trial to form a redundant code. Many granule cells were also active during movements of nearby body structures. Thus, a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.
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Affiliation(s)
- Andrea Giovannucci
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.,Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Aleksandra Badura
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.,Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
| | - Ben Deverett
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.,Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Farzaneh Najafi
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Talmo D Pereira
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Zhenyu Gao
- Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands
| | - Ilker Ozden
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.,School of Engineering, Brown University, Providence, Rhode Island, USA
| | - Alexander D Kloth
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Eftychios Pnevmatikakis
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA.,Departments of Statistics and Neuroscience, Columbia University, New York, New York, USA
| | - Liam Paninski
- Departments of Statistics and Neuroscience, Columbia University, New York, New York, USA
| | - Chris I De Zeeuw
- Netherlands Institute for Neuroscience, Amsterdam, the Netherlands.,Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands
| | - Javier F Medina
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
| | - Samuel S-H Wang
- Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
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Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, Levi R, Rodriguez FB, Varona P, Hwang E, Kim B, Han HB, Kim T, McKenna JT, Brown RE, McCarley RW, Choi JH, Rankin J, Popp PO, Rinzel J, Tabas A, Rupp A, Balaguer-Ballester E, Maturana MI, Grayden DB, Cloherty SL, Kameneva T, Ibbotson MR, Meffin H, Koren V, Lochmann T, Dragoi V, Obermayer K, Psarrou M, Schilstra M, Davey N, Torben-Nielsen B, Steuber V, Ju H, Yu J, Hines ML, Chen L, Yu Y, Kim J, Leahy W, Shlizerman E, Birgiolas J, Gerkin RC, Crook SM, Viriyopase A, Memmesheimer RM, Gielen S, Dabaghian Y, DeVito J, Perotti L, Kim AJ, Fenk LM, Cheng C, Maimon G, Zhao C, Widmer Y, Sprecher S, Senn W, Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT, Yamada Y, Steyn-Ross ML, Alistair Steyn-Ross D, Mejias JF, Murray JD, Kennedy H, Wang XJ, Kruscha A, Grewe J, Benda J, Lindner B, Badel L, Ohta K, Tsuchimoto Y, Kazama H, Kahng B, Tam ND, Pollonini L, Zouridakis G, Soh J, Kim D, Yoo M, Palmer SE, Culmone V, Bojak I, Ferrario A, Merrison-Hort R, Borisyuk R, Kim CS, Tezuka T, Joo P, Rho YA, Burton SD, Bard Ermentrout G, Jeong J, Urban NN, Marsalek P, Kim HH, Moon SH, Lee DW, Lee SB, Lee JY, Molkov YI, Hamade K, Teka W, Barnett WH, Kim T, Markin S, Rybak IA, Forro C, Dermutz H, Demkó L, Vörös J, Babichev A, Huang H, Verduzco-Flores S, Dos Santos F, Andras P, Metzner C, Schweikard A, Zurowski B, Roach JP, Sander LM, Zochowski MR, Skilling QM, Ognjanovski N, Aton SJ, Zochowski M, Wang SJ, Ouyang G, Guang J, Zhang M, Michael Wong KY, Zhou C, Robinson PA, Sanz-Leon P, Drysdale PM, Fung F, Abeysuriya RG, Rennie CJ, Zhao X, Choe Y, Yang HF, Mi Y, Lin X, Wu S, Liedtke J, Schottdorf M, Wolf F, Yamamura Y, Wickens JR, Rumbell T, Ramsey J, Reyes A, Draguljić D, Hof PR, Luebke J, Weaver CM, He H, Yang X, Ma H, Xu Z, Wang Y, Baek K, Morris LS, Kundu P, Voon V, Agnes EJ, Vogels TP, Podlaski WF, Giese M, Kuravi P, Vogels R, Seeholzer A, Podlaski W, Ranjan R, Vogels T, Torres JJ, Baroni F, Latorre R, Gips B, Lowet E, Roberts MJ, de Weerd P, Jensen O, van der Eerden J, Goodarzinick A, Niry MD, Valizadeh A, Pariz A, Parsi SS, Warburton JM, Marucci L, Tamagnini F, Brown J, Tsaneva-Atanasova K, Kleberg FI, Triesch J, Moezzi B, Iannella N, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Ridding MC, Zapotocky M, Smit D, Fouquet C, Trembleau A, Dasgupta S, Nishikawa I, Aihara K, Toyoizumi T, Robb DT, Mellen N, Toporikova N, Tang R, Tang YY, Liang G, Kiser SA, Howard JH, Goncharenko J, Voronenko SO, Ahamed T, Stephens G, Yger P, Lefebvre B, Spampinato GLB, Esposito E, et Olivier Marre MS, Choi H, Song MH, Chung S, Lee DD, Sompolinsky H, Phillips RS, Smith J, Chatzikalymniou AP, Ferguson K, Alex Cayco Gajic N, Clopath C, Angus Silver R, Gleeson P, Marin B, Sadeh S, Quintana A, Cantarelli M, Dura-Bernal S, Lytton WW, Davison A, Li L, Zhang W, Wang D, Song Y, Park S, Choi I, Shin HS, Choi H, Pasupathy A, Shea-Brown E, Huh D, Sejnowski TJ, Vogt SM, Kumar A, Schmidt R, Van Wert S, Schiff SJ, Veale R, Scheutz M, Lee SW, Gallinaro J, Rotter S, Rubchinsky LL, Cheung CC, Ratnadurai-Giridharan S, Shomali SR, Ahmadabadi MN, Shimazaki H, Nader Rasuli S, Zhao X, Rasch MJ, Wilting J, Priesemann V, Levina A, Rudelt L, Lizier JT, Spinney RE, Rubinov M, Wibral M, Bak JH, Pillow J, Zaho Y, Park IM, Kang J, Park HJ, Jang J, Paik SB, Choi W, Lee C, Song M, Lee H, Park Y, Yilmaz E, Baysal V, Ozer M, Saska D, Nowotny T, Chan HK, Diamond A, Herrmann CS, Murray MM, Ionta S, Hutt A, Lefebvre J, Weidel P, Duarte R, Morrison A, Lee JH, Iyer R, Mihalas S, Koch C, Petrovici MA, Leng L, Breitwieser O, Stöckel D, Bytschok I, Martel R, Bill J, Schemmel J, Meier K, Esler TB, Burkitt AN, Kerr RR, Tahayori B, Nolte M, Reimann MW, Muller E, Markram H, Parziale A, Senatore R, Marcelli A, Skiker K, Maouene M, Neymotin SA, Seidenstein A, Lakatos P, Sanger TD, Menzies RJ, McLauchlan C, van Albada SJ, Kedziora DJ, Neymotin S, Kerr CC, Suter BA, Shepherd GMG, Ryu J, Lee SH, Lee J, Lee HJ, Lim D, Wang J, Lee H, Jung N, Anh Quang L, Maeng SE, Lee TH, Lee JW, Park CH, Ahn S, Moon J, Choi YS, Kim J, Jun SB, Lee S, Lee HW, Jo S, Jun E, Yu S, Goetze F, Lai PY, Kim S, Kwag J, Jang HJ, Filipović M, Reig R, Aertsen A, Silberberg G, Bachmann C, Buttler S, Jacobs H, Dillen K, Fink GR, Kukolja J, Kepple D, Giaffar H, Rinberg D, Shea S, Koulakov A, Bahuguna J, Tetzlaff T, Kotaleski JH, Kunze T, Peterson A, Knösche T, Kim M, Kim H, Park JS, Yeon JW, Kim SP, Kang JH, Lee C, Spiegler A, Petkoski S, Palva MJ, Jirsa VK, Saggio ML, Siep SF, Stacey WC, Bernar C, Choung OH, Jeong Y, Lee YI, Kim SH, Jeong M, Lee J, Kwon J, Kralik JD, Jahng J, Hwang DU, Kwon JH, Park SM, Kim S, Kim H, Kim PS, Yoon S, Lim S, Park C, Miller T, Clements K, Ahn S, Ji EH, Issa FA, Baek J, Oba S, Yoshimoto J, Doya K, Ishii S, Mosqueiro TS, Strube-Bloss MF, Smith B, Huerta R, Hadrava M, Hlinka J, Bos H, Helias M, Welzig CM, Harper ZJ, Kim WS, Shin IS, Baek HM, Han SK, Richter R, Vitay J, Beuth F, Hamker FH, Toppin K, Guo Y, Graham BP, Kale PJ, Gollo LL, Stern M, Abbott LF, Fedorov LA, Giese MA, Ardestani MH, Faraji MJ, Preuschoff K, Gerstner W, van Gendt MJ, Briaire JJ, Kalkman RK, Frijns JHM, Lee WH, Frangou S, Fulcher BD, Tran PHP, Fornito A, Gliske SV, Lim E, Holman KA, Fink CG, Kim JS, Mu S, Briggman KL, Sebastian Seung H, Wegener D, Bohnenkamp L, Ernst UA, Devor A, Dale AM, Lines GT, Edwards A, Tveito A, Hagen E, Senk J, Diesmann M, Schmidt M, Bakker R, Shen K, Bezgin G, Hilgetag CC, van Albada SJ, Sun H, Sourina O, Huang GB, Klanner F, Denk C, Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G, Witek MAG, Clarke EF, Hansen M, Wallentin M, Kringelbach ML, Vuust P, Klingbeil G, De Schutter E, Chen W, Zang Y, Hong S, Takashima A, Zamora C, Gallimore AR, Goldschmidt D, Manoonpong P, Karoly PJ, Freestone DR, Soundry D, Kuhlmann L, Paninski L, Cook M, Lee J, Fishman YI, Cohen YE, Roberts JA, Cocchi L, Sweeney Y, Lee S, Jung WS, Kim Y, Jung Y, Song YK, Chavane F, Soman K, Muralidharan V, Srinivasa Chakravarthy V, Shivkumar S, Mandali A, Pragathi Priyadharsini B, Mehta H, Davey CE, Brinkman BAW, Kekona T, Rieke F, Buice M, De Pittà M, Berry H, Brunel N, Breakspear M, Marsat G, Drew J, Chapman PD, Daly KC, Bradle SP, Seo SB, Su J, Kavalali ET, Blackwell J, Shiau L, Buhry L, Basnayake K, Lee SH, Levy BA, Baker CI, Leleu T, Philips RT, Chhabria K. 25th Annual Computational Neuroscience Meeting: CNS-2016. BMC Neurosci 2016; 17 Suppl 1:54. [PMID: 27534393 PMCID: PMC5001212 DOI: 10.1186/s12868-016-0283-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. Palmer P6 A neural field model with a third dimension representing cortical depth Viviana Culmone, Ingo Bojak P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk P8 The recognition dynamics in the brain Chang Sub Kim P9 Multivariate spike train analysis using a positive definite kernel Taro Tezuka P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia Pangyu Joo P11 The ionic basis of heterogeneity affects stochastic synchrony Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban P12 Circular statistics of noise in spike trains with a periodic component Petr Marsalek P14 Representations of directions in EEG-BCI using Gaussian readouts Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong P15 Action selection and reinforcement learning in basal ganglia during reaching movements Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak P17 Axon guidance: modeling axonal growth in T-Junction assay Csaba Forro, Harald Dermutz, László Demkó, János Vörös P19 Transient cell assembly networks encode persistent spatial memories Yuri Dabaghian, Andrey Babichev P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons Haiping Huang P21 Design of biologically-realistic simulations for motor control Sergio Verduzco-Flores P22 Towards understanding the functional impact of the behavioural variability of neurons Filipa Dos Santos, Peter Andras P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia Christoph Metzner, Achim Schweikard, Bartosz Zurowski P24 Memory recall and spike frequency adaptation James P. Roach, Leonard M. Sander, Michal R. Zochowski P25 Stability of neural networks and memory consolidation preferentially occur near criticality Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou P27 Neurofield: a C++ library for fast simulation of 2D neural field models Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao P28 Action-based grounding: Beyond encoding/decoding in neural code Yoonsuck Choe, Huei-Fang Yang P29 Neural computation in a dynamical system with multiple time scales Yuanyuan Mi, Xiaohan Lin, Si Wu P30 Maximum entropy models for 3D layouts of orientation selectivity Joscha Liedtke, Manuel Schottdorf, Fred Wolf P31 A behavioral assay for probing computations underlying curiosity in rodents Yoriko Yamamura, Jeffery R. Wickens P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon P35 Dynamics of cooperative excitatory and inhibitory plasticity Everton J. Agnes, Tim P. Vogels P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons William F. Podlaski, Tim P. Vogels P37 Phenomenological neural model for adaptation of neurons in area IT Martin Giese, Pradeep Kuravi, Rufin Vogels P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona P40 Different roles for transient and sustained activity during active visual processing Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh P42 High frequency neuron can facilitate propagation of signal in neural networks Aref Pariz, Shervin S. Parsi, Alireza Valizadeh P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP Florence I. Kleberg, Jochen Triesch P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch P46 Structure and dynamics of axon network formed in primary cell culture Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau P47 Efficient signal processing and sampling in random networks that generate variability Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi P48 Modeling the effect of riluzole on bursting in respiratory neural networks Daniel T. Robb, Nick Mellen, Natalia Toporikova P49 Mapping relaxation training using effective connectivity analysis Rongxiang Tang, Yi-Yuan Tang P50 Modeling neuron oscillation of implicit sequence learning Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber P52 Nonlinear response of noisy neurons Sergej O. Voronenko, Benjamin Lindner P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion Tosif Ahamed, Greg Stephens P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre P55 Sufficient sampling rates for fast hand motion tracking Hansol Choi, Min-Ho Song P56 Linear readout of object manifolds SueYeon Chung, Dan D. Lee, Haim Sompolinsky P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves Ryan S. Phillips, Jeffrey Smith P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver P60 A set of curated cortical models at multiple scales on Open Source Brain Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver P61 A synaptic story of dynamical information encoding in neural adaptation Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu P62 Physical modeling of rule-observant rodent behavior Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes Hannah Choi, Anitha Pasupathy, Eric Shea-Brown P65 Stability of FORCE learning on spiking and rate-based networks Dongsung Huh, Terrence J. Sejnowski P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments Simon M. Vogt, Arvind Kumar, Robert Schmidt P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation Stephen Van Wert, Steven J. Schiff P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo Richard Veale, Matthias Scheutz P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions Sang Wan Lee P70 Maturation of sensory networks through homeostatic structural plasticity Júlia Gallinaro, Stefan Rotter P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon, Peter A. Robinson P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan P73 Exact spike-timing distribution reveals higher-order interactions of neurons Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli P74 Neural mechanism of visual perceptual learning using a multi-layered neural network Xiaochen Zhao, Malte J. Rasch P75 Inferring collective spiking dynamics from mostly unobserved systems Jens Wilting, Viola Priesemann P76 How to infer distributions in the brain from subsampled observations Anna Levina, Viola Priesemann P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons Lucas Rudelt, Joseph T. Lizier, Viola Priesemann P78 A nearest-neighbours based estimator for transfer entropy between spike trains Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann P79 Active learning of psychometric functions with multinomial logistic models Ji Hyun Bak, Jonathan Pillow P81 Inferring low-dimensional network dynamics with variational latent Gaussian process Yuan Zaho, Il Memming Park P82 Computational investigation of energy landscapes in the resting state subcortical brain network Jiyoung Kang, Hae-Jeong Park P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map Jaeson Jang, Se-Bum Paik P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition Woochul Choi, Se-Bum Paik P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map Changju Lee, Jaeson Jang, Se-Bum Paik P86 Computational method classifying neural network activity patterns for imaging data Min Song, Hyeonsu Lee, Se-Bum Paik P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory Youngjin Park, Woochul Choi, Se-Bum Paik P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons Ergin Yilmaz, Veli Baysal, Mahmut Ozer P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance Veronika Koren, Klaus Obermayer P90 Methods for building accurate models of individual neurons Daniel Saska, Thomas Nowotny P91 A full size mathematical model of the early olfactory system of honeybees Ho Ka Chan, Alan Diamond, Thomas Nowotny P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input Philipp Weidel, Renato Duarte, Abigail Morrison P94 Modulation of tuning induced by abrupt reduction of SST cell activity Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas P95 The functional role of VIP cell activation during locomotion Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas P96 Stochastic inference with spiking neural networks Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier P97 Modeling orientation-selective electrical stimulation with retinal prostheses Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin P98 Ion channel noise can explain firing correlation in auditory nerves Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram P100 On the representation of arm reaching movements: a computational model Antonio Parziale, Rosa Senatore, Angelo Marcelli P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior Rosa Senatore, Antonio Parziale, Angelo Marcelli P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge K. Skiker, M. Maouene P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton P104 Effect of network size on computational capacity Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas Juhyoung Ryu, Sang-Hun Lee P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception Joonwon Lee, Sang-Hun Lee P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making Hyang Jung Lee, Sang-Hun Lee P110 A Bayesian algorithm for phoneme Perception and its neural implementation Daeseob Lim, Sang-Hun Lee P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol Jisung Wang, Heonsoo Lee P112 Self-organized criticality of neural avalanche in a neural model on complex networks Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee P114 Computational model to replicate seizure suppression effect by electrical stimulation Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy Felix Goetze, Pik-Yin Lai P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell Seonghyun Kim, Jeehyun Kwag P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model Hyun Jae Jang, Jeehyun Kwag P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison P120 Learning sparse representations in the olfactory bulb Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov P121 Functional classification of homologous basal-ganglia networks Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski P122 Short term memory based on multistability Tim Kunze, Andre Peterson, Thomas Knösche P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units Minjung Kim, Hojeong Kim P125 Decoding laser-induced somatosensory information from EEG Ji Sung Park, Ji Won Yeon, Sung-Phil Kim P126 Phase synchronization of alpha activity for EEG-based personal authentication Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa P130 Epileptic seizures in the unfolding of a codimension-3 singularity Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa P131 Incremental dimensional exploratory reasoning under multi-dimensional environment Oh-hyeon Choung, Yong Jeong P132 A low-cost model of eye movements and memory in personal visual cognition Yong-il Lee, Jaeseung Jeong P133 Complex network analysis of structural connectome of autism spectrum disorder patients Su Hyun Kim, Mir Jeong, Jaeseung Jeong P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong P136 Detecting purchase decision based on hyperfrontality of the EEG Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it? Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta P146 Swinging networks Michal Hadrava, Jaroslav Hlinka P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions Hannah Bos, Moritz Helias P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning Charles M. Welzig, Zachary J. Harper P149 Multiscale complexity analysis for the segmentation of MRI images Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han P150 A neuro-computational model of emotional attention René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker P151 Multi-site delayed feedback stimulation in parkinsonian networks Kelly Toppin, Yixin Guo P152 Bistability in Hodgkin–Huxley-type equations Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency Mark D. McDonnell, Bruce P. Graham P154 Quantifying resilience patterns in brain networks: the importance of directionality Penelope J. Kale, Leonardo L. Gollo P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations Merav Stern, L. F. Abbott P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues Leonid A. Fedorov, Martin A. Giese P157 Spiking model for the interaction between action recognition and action execution Mohammad Hovaidi Ardestani, Martin Giese P158 Surprise-modulated belief update: how to learn within changing environments? Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks Won Hee Lee, Sophia Frangou P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen P167 Local field potentials in a 4 × 4 mm2 multi-layered network model Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann P168 A spiking network model explains multi-scale properties of cortical dynamics Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk P170 Tensor decomposition reveals RSNs in simulated resting state fMRI Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors Guido Klingbeil, Erik De Schutter P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS Weiliang Chen, Erik De Schutter P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input Yunliang Zang, Erik De Schutter P175 Dendritic morphology determines how dendrites are organized into functional subunits Sungho Hong, Akira Takashima, Erik De Schutter P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells Criseida Zamora, Andrew R. Gallimore, Erik De Schutter P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta P178 Data-driven neural models part II: connectivity patterns of human seizures Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook P179 Data-driven neural models part I: state and parameter estimation Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook P180 Spectral and spatial information processing in human auditory streaming Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain Leonardo L. Gollo, James A. Roberts, Luca Cocchi P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles Yann Sweeney, Claudia Clopath P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi P186 Neural field model of localized orientation selective activation in V1 James Rankin, Frédéric Chavane P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P189 A computational architecture to model the microanatomy of the striatum and its functional properties Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy P191 Emergence of radial orientation selectivity from synaptic plasticity Catherine E. Davey, David B. Grayden, Anthony N. Burkitt P192 How do hidden units shape effective connections between neurons? Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice P193 Characterization of neural firing in the presence of astrocyte-synapse signaling Maurizio De Pittà, Hugues Berry, Nicolas Brunel P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics James A. Roberts, Leonardo L. Gollo, Michael Breakspear P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons LieJune Shiau, Laure Buhry, Kanishka Basnayake P200 Visual face representations during memory retrieval compared to perception Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics Timothée Leleu, Kazuyuki Aihara Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
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Merel J, Shababo B, Naka A, Adesnik H, Paninski L. Bayesian methods for event analysis of intracellular currents. J Neurosci Methods 2016; 269:21-32. [DOI: 10.1016/j.jneumeth.2016.05.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 05/13/2016] [Accepted: 05/16/2016] [Indexed: 01/04/2023]
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Abstract
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
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Affiliation(s)
- Josh Merel
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - David Carlson
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - John P. Cunningham
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
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Gabitto MI, Pakman A, Bikoff JB, Abbott LF, Jessell TM, Paninski L. Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons. Cell 2016; 165:220-233. [PMID: 26949187 DOI: 10.1016/j.cell.2016.01.026] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Revised: 11/30/2015] [Accepted: 01/15/2016] [Indexed: 12/14/2022]
Abstract
Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of ~50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell-type characterization in the nervous system and elsewhere.
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Affiliation(s)
- Mariano I Gabitto
- Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute for Brain Science, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA.
| | - Ari Pakman
- Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Jay B Bikoff
- Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute for Brain Science, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA
| | - L F Abbott
- Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA
| | - Thomas M Jessell
- Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute for Brain Science, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA
| | - Liam Paninski
- Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA.
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Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP, Chichilnisky EJ. Mapping nonlinear receptive field structure in primate retina at single cone resolution. eLife 2015; 4. [PMID: 26517879 PMCID: PMC4623615 DOI: 10.7554/elife.05241] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2014] [Accepted: 09/07/2015] [Indexed: 11/13/2022] Open
Abstract
The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits. DOI:http://dx.doi.org/10.7554/eLife.05241.001 Light that enters the eye begins the process of vision by activating two types of photoreceptors: rods, which support vision under low light levels, and cones, which are responsible for fine detail and color vision. Activation of either type of photoreceptor triggers responses in bipolar cells, which activate the ganglion cells that transmit visual signals to the brain. Bipolar cells therefore belong to a class of cells called interneurons, which relay information from certain cell types to others. Interneurons play an important role in information processing throughout the brain, but directly accessing them or characterizing their role in neural computation is often difficult. To address this problem, Freeman, Field et al. have developed a combined computational and experimental approach to describe the flow of sensory signals through the circuits within the retina of primates. Large arrays of electrodes were used to record the responses of many retinal ganglion cells in response to the activation or de-activation of pairs of cones. These experiments revealed that the responses of ganglion cells are not simply the sum of the inputs that they receive from cones; specifically, the activation of one cone is not cancelled by the deactivation of another. Instead, the data suggest that bipolar cells add cone inputs together and then pass on the total activation (but not deactivation) to ganglion cells. By analyzing the responses of ganglion cells to numerous random patterns of cone activation, Freeman, Field et al. were able to estimate the locations and arrangements of bipolar cells that connect to them. These predicted patterns of connectivity agreed with observations from anatomical studies. This work provides detailed insights into how the primate retina works. It also suggests that similar approaches may be used to characterize how signals flow across other brain networks in which large-scale recordings are now possible. DOI:http://dx.doi.org/10.7554/eLife.05241.002
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Affiliation(s)
- Jeremy Freeman
- Janelia Research Center, Howard Hughes Medical Institute, Ashburn, United States.,Center for Neural Science, New York, United States
| | - Greg D Field
- Department of Neurobiology, Duke University School of Medicine, Durham, United States.,Salk Institute for Biological Studies, La Jolla, United States
| | - Peter H Li
- Salk Institute for Biological Studies, La Jolla, United States
| | - Martin Greschner
- Salk Institute for Biological Studies, La Jolla, United States.,Department of Neuroscience, University of Oldenburg, Oldenburg, Germany
| | - Deborah E Gunning
- Institute of Photonics, University of Strathclyde, Glasgow, United Kingdom
| | - Keith Mathieson
- Institute of Photonics, University of Strathclyde, Glasgow, United Kingdom
| | - Alexander Sher
- Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, United States
| | - Alan M Litke
- Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, United States
| | - Liam Paninski
- Department of Statistics, Columbia University, Columbia, United States
| | - Eero P Simoncelli
- Center for Neural Science, Courant Institute of Mathematical Sciences, New York, United States
| | - E J Chichilnisky
- Salk Institute for Biological Studies, La Jolla, United States.,Department of Neurosurgery, Stanford School of Medicine, Stanford, United States
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Soudry D, Keshri S, Stinson P, Oh MH, Iyengar G, Paninski L. Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. PLoS Comput Biol 2015; 11:e1004464. [PMID: 26465147 PMCID: PMC4605541 DOI: 10.1371/journal.pcbi.1004464] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 07/09/2015] [Indexed: 11/19/2022] Open
Abstract
Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The “common input” problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons. Available techniques allow us to simultaneously record, with sufficient temporal resolution, only a small fraction of the network. Consequently, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a “shotgun” experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe much larger subsets of the network over the course of the entire experiment, thus ameliorating the common input problem. Using a generalized linear model for a spiking recurrent neural network, we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects. Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, can be quickly and accurately estimated, achieving orders of magnitude speed up over previous approaches. Optical imaging of the activity in a neuronal network is limited by the scanning speed of the imaging device. Therefore, typically, only a small fixed part of the network is observed during the entire experiment. However, in such an experiment, it can be hard to infer from the observed activity patterns whether (1) a neuron A directly affects neuron B, or (2) another, unobserved neuron C affects both A and B. To deal with this issue, we propose a “shotgun” observation scheme, in which, at each time point, we observe a small changing subset of the neurons from the network. Consequently, many fewer neurons remain completely unobserved during the entire experiment, enabling us to eventually distinguish between cases (1) and (2) given sufficiently long experiments. Since previous inference algorithms cannot efficiently handle so many missing observations, we develop a scalable algorithm for data acquired using the shotgun observation scheme, in which only a small fraction of the neurons are observed in each time bin. Using this kind of simulated data, we show the algorithm is able to quickly infer connectivity in spiking recurrent networks with thousands of neurons.
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Affiliation(s)
- Daniel Soudry
- Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America
| | - Suraj Keshri
- Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America
| | - Patrick Stinson
- Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America
| | - Min-Hwan Oh
- Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America
| | - Garud Iyengar
- Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America
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Machado TA, Pnevmatikakis E, Paninski L, Jessell TM, Miri A. Primacy of Flexor Locomotor Pattern Revealed by Ancestral Reversion of Motor Neuron Identity. Cell 2015; 162:338-350. [PMID: 26186188 PMCID: PMC4540486 DOI: 10.1016/j.cell.2015.06.036] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 04/01/2015] [Accepted: 05/21/2015] [Indexed: 02/07/2023]
Abstract
Spinal circuits can generate locomotor output in the absence of sensory or descending input, but the principles of locomotor circuit organization remain unclear. We sought insight into these principles by considering the elaboration of locomotor circuits across evolution. The identity of limb-innervating motor neurons was reverted to a state resembling that of motor neurons that direct undulatory swimming in primitive aquatic vertebrates, permitting assessment of the role of motor neuron identity in determining locomotor pattern. Two-photon imaging was coupled with spike inference to measure locomotor firing in hundreds of motor neurons in isolated mouse spinal cords. In wild-type preparations, we observed sequential recruitment of motor neurons innervating flexor muscles controlling progressively more distal joints. Strikingly, after reversion of motor neuron identity, virtually all firing patterns became distinctly flexor like. Our findings show that motor neuron identity directs locomotor circuit wiring and indicate the evolutionary primacy of flexor pattern generation.
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Affiliation(s)
- Timothy A Machado
- Departments of Neuroscience and Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute of Brain Science, Columbia University, New York, NY 10032, USA; Department of Statistics, Center for Theoretical Neuroscience and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA.
| | - Eftychios Pnevmatikakis
- Department of Statistics, Center for Theoretical Neuroscience and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA
| | - Liam Paninski
- Department of Statistics, Center for Theoretical Neuroscience and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Thomas M Jessell
- Departments of Neuroscience and Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute of Brain Science, Columbia University, New York, NY 10032, USA.
| | - Andrew Miri
- Departments of Neuroscience and Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute of Brain Science, Columbia University, New York, NY 10032, USA
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Abstract
Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.
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Affiliation(s)
- Josh Merel
- Neurobiology and Behavior Program, Columbia University, New York, New York, United States of America
| | - Donald M. Pianto
- Statistics Department, Columbia University, New York, New York, United States of America
- Statistics Department, University of Brasília, Brasília, Distrito Federal, Brazil
| | - John P. Cunningham
- Statistics Department, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Statistics Department, Columbia University, New York, New York, United States of America
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48
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Abstract
Parametric models of the conditional intensity of a point process (e.g., generalized linear models) are popular in statistical neuroscience, as they allow us to characterize the variability in neural responses in terms of stimuli and spiking history. Parameter estimation in these models relies heavily on accurate evaluations of the log likelihood and its derivatives. Classical approaches use a discretized time version of the spiking process, and recent work has exploited the existence of a refractory period (during which the conditional intensity is zero following a spike) to obtain more accurate estimates of the likelihood. In this brief letter, we demonstrate that this method can be improved significantly by applying classical quadrature methods directly to the resulting continuous-time integral.
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Affiliation(s)
- Gonzalo Mena
- Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, U.S.A.
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Pnevmatikakis EA, Rad KR, Huggins J, Paninski L. Fast Kalman Filtering and Forward–Backward Smoothing via a Low-Rank Perturbative Approach. J Comput Graph Stat 2014. [DOI: 10.1080/10618600.2012.760461] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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