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Monsalve-Mercado MM, Miller KD. The geometry of the neural state space of decisions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.24.634806. [PMID: 39896602 PMCID: PMC11785246 DOI: 10.1101/2025.01.24.634806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
How do populations of neurons collectively encode and process information during cognitive tasks? We analyze high-yield population recordings from the macaque lateral intraparietal area (LIP) during a reaction-time random-dot-motion direction-discrimination task. We find that the trajectories of neural population activity patterns during single decisions lie within a curved two-dimensional manifold. The reaction time of trajectories systematically varies along one dimension, such that slow and fast decisions trace distinct activity patterns. Trajectories transition from a deliberation stage, in which they are noisy and remain similar between the choices, to a commitment stage, in which they are far less noisy and diverge sharply for the different choices. The deliberation phase is pronounced for slower decisions and gradually diminishes as reaction time decreases. A mechanistic circuit model provides an explanation for the observed properties, and suggests the transition between stages represents a transition from more sensory-driven to more circuit-driven dynamics. It yields two striking predictions we verify in the data. First, whether neurons are more choice selective for slow or fast trials varies systematically with the retinotopic location of their response fields. Second, the slower the trial, the more saccades undershoot the choice target. The results highlight the roles of distributed and dynamic activity patterns and intrinsic circuit dynamics in the population implementation of a cognitive task.
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Affiliation(s)
- Mauro M. Monsalve-Mercado
- Center for Theoretical Neuroscience and Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
| | - Kenneth D. Miller
- Center for Theoretical Neuroscience and Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
- Dept. of Neuroscience and Swartz Program in Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York
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2
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Geng J, Voitiuk K, Parks DF, Robbins A, Spaeth A, Sevetson JL, Hernandez S, Schweiger HE, Andrews JP, Seiler ST, Elliott MA, Chang EF, Nowakowski TJ, Currie R, Mostajo-Radji MA, Haussler D, Sharf T, Salama SR, Teodorescu M. Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.14.623530. [PMID: 39605518 PMCID: PMC11601321 DOI: 10.1101/2024.11.14.623530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.
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Affiliation(s)
- Jinghui Geng
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Kateryna Voitiuk
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - David F. Parks
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ash Robbins
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alex Spaeth
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Jessica L. Sevetson
- Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Sebastian Hernandez
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Hunter E. Schweiger
- Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - John P. Andrews
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Spencer T. Seiler
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Matthew A.T. Elliott
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Edward F. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA
| | - Tomasz J. Nowakowski
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA
| | - Rob Currie
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - David Haussler
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Tal Sharf
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Sofie R. Salama
- Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mircea Teodorescu
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Lead Contact
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3
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Xu C, Nedergaard M, Fowell DJ, Friedl P, Ji N. Multiphoton fluorescence microscopy for in vivo imaging. Cell 2024; 187:4458-4487. [PMID: 39178829 PMCID: PMC11373887 DOI: 10.1016/j.cell.2024.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 08/26/2024]
Abstract
Multiphoton fluorescence microscopy (MPFM) has been a game-changer for optical imaging, particularly for studying biological tissues deep within living organisms. MPFM overcomes the strong scattering of light in heterogeneous tissue by utilizing nonlinear excitation that confines fluorescence emission mostly to the microscope focal volume. This enables high-resolution imaging deep within intact tissue and has opened new avenues for structural and functional studies. MPFM has found widespread applications and has led to numerous scientific discoveries and insights into complex biological processes. Today, MPFM is an indispensable tool in many research communities. Its versatility and effectiveness make it a go-to technique for researchers investigating biological phenomena at the cellular and subcellular levels in their native environments. In this Review, the principles, implementations, capabilities, and limitations of MPFM are presented. Three application areas of MPFM, neuroscience, cancer biology, and immunology, are reviewed in detail and serve as examples for applying MPFM to biological research.
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Affiliation(s)
- Chris Xu
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14850, USA
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Alle 3B, 2200 Copenhagen, Denmark; University of Rochester Medical School, 601 Elmwood Avenue, Rochester, NY 14642, USA
| | - Deborah J Fowell
- Department of Microbiology & Immunology, Cornell University, Ithaca, NY 14853, USA
| | - Peter Friedl
- Department of Medical BioSciences, Radboud University Medical Centre, Geert Grooteplein 26-28, Nijmegen HB 6500, the Netherlands
| | - Na Ji
- Department of Neuroscience, Department of Physics, University of California Berkeley, Berkeley, CA 94720, USA.
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4
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Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner MM, 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. Nat Methods 2024; 21:1316-1328. [PMID: 38918605 DOI: 10.1038/s41592-024-02319-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/17/2024] [Indexed: 06/27/2024]
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 released a cloud application that allows users to label data, train networks and process new videos directly from the browser.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Anup Khanal
- University of California, Los Angeles, Los Angeles, CA, USA
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5
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 PMCID: PMC11180540 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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6
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Mosberger AC, Sibener LJ, Chen TX, Rodrigues HFM, Hormigo R, Ingram JN, Athalye VR, Tabachnik T, Wolpert DM, Murray JM, Costa RM. Exploration biases forelimb reaching strategies. Cell Rep 2024; 43:113958. [PMID: 38520691 PMCID: PMC11097405 DOI: 10.1016/j.celrep.2024.113958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/05/2023] [Accepted: 02/28/2024] [Indexed: 03/25/2024] Open
Abstract
The brain can generate actions, such as reaching to a target, using different movement strategies. We investigate how such strategies are learned in a task where perched head-fixed mice learn to reach to an invisible target area from a set start position using a joystick. This can be achieved by learning to move in a specific direction or to a specific endpoint location. As mice learn to reach the target, they refine their variable joystick trajectories into controlled reaches, which depend on the sensorimotor cortex. We show that individual mice learned strategies biased to either direction- or endpoint-based movements. This endpoint/direction bias correlates with spatial directional variability with which the workspace was explored during training. Model-free reinforcement learning agents can generate both strategies with similar correlation between variability during training and learning bias. These results provide evidence that reinforcement of individual exploratory behavior during training biases the reaching strategies that mice learn.
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Affiliation(s)
- Alice C Mosberger
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Leslie J Sibener
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Tiffany X Chen
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Helio F M Rodrigues
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Allen Institute, Seattle, WA 98109, USA
| | - Richard Hormigo
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - James N Ingram
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Vivek R Athalye
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Tanya Tabachnik
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Daniel M Wolpert
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - James M Murray
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Rui M Costa
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Allen Institute, Seattle, WA 98109, USA.
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7
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Lee KH, Denovellis EL, Ly R, Magland J, Soules J, Comrie AE, Gramling DP, Guidera JA, Nevers R, Adenekan P, Brozdowski C, Bray SR, Monroe E, Bak JH, Coulter ME, Sun X, Broyles E, Shin D, Chiang S, Holobetz C, Tritt A, Rübel O, Nguyen T, Yatsenko D, Chu J, Kemere C, Garcia S, Buccino A, Frank LM. Spyglass: a framework for reproducible and shareable neuroscience research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.577295. [PMID: 38328074 PMCID: PMC10849637 DOI: 10.1101/2024.01.25.577295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible. To address these challenges, we created Spyglass, an open-source software framework that enables reproducible analyses and sharing of data and both intermediate and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for several core analyses in neuroscience, including spectral filtering, spike sorting, pose tracking, and neural decoding. It can be easily extended to apply both existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.
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Affiliation(s)
- Kyu Hyun Lee
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Eric L. Denovellis
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Ryan Ly
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | - Jeremy Magland
- Center for Computational Mathematics, Flatiron Institute
| | - Jeff Soules
- Center for Computational Mathematics, Flatiron Institute
| | - Alison E. Comrie
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Daniel P. Gramling
- Graudate Program in Neural and Behavioral Sciences, University of Tübingen
| | - Jennifer A. Guidera
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco
- Medical Scientist Training Program, University of California, San Francisco
| | - Rhino Nevers
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Philip Adenekan
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Chris Brozdowski
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Samuel R. Bray
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Emily Monroe
- Department of Physiology, University of California, San Francisco
| | - Ji Hyun Bak
- Department of Physiology, University of California, San Francisco
| | - Michael E. Coulter
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Xulu Sun
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Emrey Broyles
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Donghoon Shin
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco
| | - Sharon Chiang
- Department of Neurology, University of California, San Francisco
| | | | - Andrew Tritt
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | - Oliver Rübel
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | | | | | - Joshua Chu
- Department of Electrical and Computer Engineering, Rice University
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University
| | | | | | - Loren M. Frank
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
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8
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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 : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538703. [PMID: 37162966 PMCID: PMC10168383 DOI: 10.1101/2023.04.28.538703] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [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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9
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Van Horn JD. Editorial: On the Economics of Neuroscientific Data Sharing. Neuroinformatics 2024; 22:1-4. [PMID: 37966621 DOI: 10.1007/s12021-023-09649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, USA.
- School of Data Science, University of Virginia, Charlottesville, VA, USA.
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10
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Buccino AP, Winter O, Bryant D, Feng D, Svoboda K, Siegle JH. Compression strategies for large-scale electrophysiology data. J Neural Eng 2023; 20:056009. [PMID: 37651998 DOI: 10.1088/1741-2552/acf5a4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.
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Affiliation(s)
- Alessio P Buccino
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | | | - David Bryant
- Independent Researcher, San Francisco, CA, United States of America
| | - David Feng
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Karel Svoboda
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Joshua H Siegle
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
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11
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de Vries SEJ, Siegle JH, Koch C. Sharing neurophysiology data from the Allen Brain Observatory. eLife 2023; 12:e85550. [PMID: 37432073 PMCID: PMC10335829 DOI: 10.7554/elife.85550] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/27/2023] [Indexed: 07/12/2023] Open
Abstract
Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
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12
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Mosberger AC, Sibener LJ, Chen TX, Rodrigues H, Hormigo R, Ingram JN, Athalye VR, Tabachnik T, Wolpert DM, Murray JM, Costa RM. Exploration biases how forelimb reaches to a spatial target are learned. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539291. [PMID: 37214823 PMCID: PMC10197595 DOI: 10.1101/2023.05.08.539291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The brain can learn to generate actions, such as reaching to a target, using different movement strategies. Understanding how different variables bias which strategies are learned to produce such a reach is important for our understanding of the neural bases of movement. Here we introduce a novel spatial forelimb target task in which perched head-fixed mice learn to reach to a circular target area from a set start position using a joystick. These reaches can be achieved by learning to move into a specific direction or to a specific endpoint location. We find that mice gradually learn to successfully reach the covert target. With time, they refine their initially exploratory complex joystick trajectories into controlled targeted reaches. The execution of these controlled reaches depends on the sensorimotor cortex. Using a probe test with shifting start positions, we show that individual mice learned to use strategies biased to either direction or endpoint-based movements. The degree of endpoint learning bias was correlated with the spatial directional variability with which the workspace was explored early in training. Furthermore, we demonstrate that reinforcement learning model agents exhibit a similar correlation between directional variability during training and learned strategy. These results provide evidence that individual exploratory behavior during training biases the control strategies that mice use to perform forelimb covert target reaches.
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13
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Zhu F, Grier HA, Tandon R, Cai C, Agarwal A, Giovannucci A, Kaufman MT, Pandarinath C. A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution. Nat Neurosci 2022; 25:1724-1734. [PMID: 36424431 PMCID: PMC9825112 DOI: 10.1038/s41593-022-01189-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/23/2022] [Indexed: 11/26/2022]
Abstract
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.
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Affiliation(s)
- Feng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
| | - Harrison A Grier
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Raghav Tandon
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Changjia Cai
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | | | - Andrea Giovannucci
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Closed-Loop Engineering for Advanced Rehabilitation (CLEAR), North Carolina State University, Raleigh, NC, USA.
| | - Matthew T Kaufman
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA.
- Neuroscience Institute, The University of Chicago, Chicago, IL, USA.
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA.
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14
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Vogt N. Neuroscience data analysis in the cloud. Nat Methods 2022; 19:1034. [PMID: 36068316 DOI: 10.1038/s41592-022-01613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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