1
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Sihn D, Kim SP. Disruption of alpha oscillation propagation in patients with schizophrenia. Clin Neurophysiol 2024; 162:262-270. [PMID: 38480063 DOI: 10.1016/j.clinph.2024.02.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/18/2024] [Accepted: 02/17/2024] [Indexed: 05/19/2024]
Abstract
OBJECTIVE Propagation of electroencephalogram (EEG) oscillations, often referred to as traveling waves, reflects the role of brain oscillations in neural information transmission. This propagation can be distorted by brain disorders such as schizophrenia that features disconnection of neural information transmission (i.e., disconnection syndrome). However, this possibility of the disruption of EEG oscillation propagation in patients with schizophrenia remains largely unexplored. METHODS Using a publicly shared dataset (N = 19 and 24; patients with schizophrenia and healthy controls, respectively), we investigated EEG oscillation propagation by analyzing the local phase gradients (LPG) of alpha (8-12 Hz) oscillations in both healthy participants and patients with schizophrenia. RESULTS Our results showed significant directionality in the propagation of alpha oscillations in healthy participants. Specifically, alpha oscillations propagated in an anterior-to-posterior direction along mid-line and a posterior-to-anterior direction laterally. In patients with schizophrenia, some of alpha oscillation propagation were notably disrupted, particularly in the central midline area where alpha oscillations propagated from anterior to posterior areas. CONCLUSION Our finding lends support to the hypothesis of a disconnection syndrome in schizophrenia, underscoring a disruption in the anterior-to-posterior propagation of alpha oscillations. SIGNIFICANCE This study identified disruption of alpha oscillation propagation observed in scalp EEG as a biomarker for schizophrenia.
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Affiliation(s)
- Duho Sihn
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
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2
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Wolff M, Halassa MM. The mediodorsal thalamus in executive control. Neuron 2024; 112:893-908. [PMID: 38295791 DOI: 10.1016/j.neuron.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/15/2023] [Accepted: 01/03/2024] [Indexed: 03/23/2024]
Abstract
Executive control, the ability to organize thoughts and action plans in real time, is a defining feature of higher cognition. Classical theories have emphasized cortical contributions to this process, but recent studies have reinvigorated interest in the role of the thalamus. Although it is well established that local thalamic damage diminishes cognitive capacity, such observations have been difficult to inform functional models. Recent progress in experimental techniques is beginning to enrich our understanding of the anatomical, physiological, and computational substrates underlying thalamic engagement in executive control. In this review, we discuss this progress and particularly focus on the mediodorsal thalamus, which regulates the activity within and across frontal cortical areas. We end with a synthesis that highlights frontal thalamocortical interactions in cognitive computations and discusses its functional implications in normal and pathological conditions.
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Affiliation(s)
- Mathieu Wolff
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France.
| | - Michael M Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA; Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA.
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3
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Chen D, Axmacher N, Wang L. Grid codes underlie multiple cognitive maps in the human brain. Prog Neurobiol 2024; 233:102569. [PMID: 38232782 DOI: 10.1016/j.pneurobio.2024.102569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes-empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.
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Affiliation(s)
- Dong Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, 44801, Bochum, Germany
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China.
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4
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Fakhar K, Dixit S, Hadaeghi F, Kording KP, Hilgetag CC. Downstream network transformations dissociate neural activity from causal functional contributions. Sci Rep 2024; 14:2103. [PMID: 38267481 PMCID: PMC10808222 DOI: 10.1038/s41598-024-52423-7] [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: 10/06/2023] [Accepted: 01/18/2024] [Indexed: 01/26/2024] Open
Abstract
Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neural networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.
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Affiliation(s)
- Kayson Fakhar
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany.
| | - Shrey Dixit
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany
| | - Fatemeh Hadaeghi
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany
| | - Konrad P Kording
- Departments of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Learning in Machines & Brains, CIFAR, Toronto, ON, Canada
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, MA, USA
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5
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Ding X, Froudist-Walsh S, Jaramillo J, Jiang J, Wang XJ. Cell type-specific connectome predicts distributed working memory activity in the mouse brain. eLife 2024; 13:e85442. [PMID: 38174734 PMCID: PMC10807864 DOI: 10.7554/elife.85442] [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: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.
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Affiliation(s)
- Xingyu Ding
- Center for Neural Science, New York UniversityNew YorkUnited States
| | - Sean Froudist-Walsh
- Center for Neural Science, New York UniversityNew YorkUnited States
- Bristol Computational Neuroscience Unit, School of Engineering Mathematics and Technology, University of BristolBristolUnited Kingdom
| | - Jorge Jaramillo
- Center for Neural Science, New York UniversityNew YorkUnited States
- Campus Institute for Dynamics of Biological Networks, University of GöttingenGöttingenGermany
| | - Junjie Jiang
- Center for Neural Science, New York UniversityNew YorkUnited States
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,Institute of Health and Rehabilitation Science,School of Life Science and Technology, Research Center for Brain-inspired Intelligence, Xi’an Jiaotong UniversityXi'anChina
| | - Xiao-Jing Wang
- Center for Neural Science, New York UniversityNew YorkUnited States
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6
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Xue F, Li F, Zhang KM, Ding L, Wang Y, Zhao X, Xu F, Zhang D, Sun M, Lau PM, Zhu Q, Zhou P, Bi GQ. Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes. Natl Sci Rev 2024; 11:nwad294. [PMID: 38288367 PMCID: PMC10824555 DOI: 10.1093/nsr/nwad294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/26/2023] [Accepted: 11/23/2023] [Indexed: 01/31/2024] Open
Abstract
To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.
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Affiliation(s)
- Feng Xue
- Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Fei Li
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ke-ming Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Lufeng Ding
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Yang Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Xingtao Zhao
- Department of Modern Life Sciences and Biotecnology, Xiongan Institute of Innovation, Xiongan New Area, Xiongan 071899, China
| | - Fang Xu
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | - Danke Zhang
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Mingzhai Sun
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Pak-Ming Lau
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Qingyuan Zhu
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Pengcheng Zhou
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Guo-Qiang Bi
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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7
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Markicevic M, Sturman O, Bohacek J, Rudin M, Zerbi V, Fulcher BD, Wenderoth N. Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions. eLife 2023; 12:e78620. [PMID: 37824184 PMCID: PMC10569790 DOI: 10.7554/elife.78620] [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: 03/14/2022] [Accepted: 09/21/2023] [Indexed: 10/13/2023] Open
Abstract
Understanding how the brain's macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here, we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit in mice, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial caudate putamen (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, changes in local dynamics were strongest in unimodal regions (which process information from a single sensory modality) and weakened along a hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results show that targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, such as by making BOLD dynamics more predictable over time by increasing its self-correlation structure. This contributes to ongoing attempts to understand the influence of structure-function relationships in shaping inter-regional communication at subcortical and cortical levels.
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Affiliation(s)
- Marija Markicevic
- Neural Control of Movement Lab, HEST, ETH ZürichZurichSwitzerland
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
- Department of Radiology and Biomedical Imaging, School of Medicine, Yale UniversityNew HavenUnited States
| | - Oliver Sturman
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
- Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, HEST, ETH ZurichZurichSwitzerland
| | - Johannes Bohacek
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
- Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, HEST, ETH ZurichZurichSwitzerland
| | - Markus Rudin
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
- Institute for Biomedical Engineering, University and ETH ZurichZurichSwitzerland
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFLLausanneSwitzerland
- CIBM Centre for Biomedical ImagingLausanneSwitzerland
| | - Ben D Fulcher
- School of Physics, The University of SydneyCamperdownAustralia
| | - Nicole Wenderoth
- Neural Control of Movement Lab, HEST, ETH ZürichZurichSwitzerland
- Neuroscience Center Zurich, University and ETH ZurichZurichSwitzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE)SingaporeSingapore
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8
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Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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9
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Banaie Boroujeni K, Womelsdorf T. Routing states transition during oscillatory bursts and attentional selection. Neuron 2023; 111:2929-2944.e11. [PMID: 37463578 PMCID: PMC10529654 DOI: 10.1016/j.neuron.2023.06.012] [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: 03/26/2023] [Revised: 05/22/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023]
Abstract
Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.
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Affiliation(s)
- Kianoush Banaie Boroujeni
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, USA
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10
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Safaai H, Wang AY, Kira S, Malerba SB, Panzeri S, Harvey CD. Specialized structure of neural population codes in parietal cortex outputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554635. [PMID: 37662297 PMCID: PMC10473762 DOI: 10.1101/2023.08.24.554635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.
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Affiliation(s)
- Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alice Y. Wang
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Simone Blanco Malerba
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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11
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Sanati S, Rouhani M, Hodtani GA. Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method. Front Comput Neurosci 2023; 17:1140782. [PMID: 37351534 PMCID: PMC10282945 DOI: 10.3389/fncom.2023.1140782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/17/2023] [Indexed: 06/24/2023] Open
Abstract
Hierarchical Temporal Memory (HTM) is an unsupervised algorithm in machine learning. It models several fundamental neocortical computational principles. Spatial Pooler (SP) is one of the main components of the HTM, which continuously encodes streams of binary input from various layers and regions into sparse distributed representations. In this paper, the goal is to evaluate the sparsification in the SP algorithm from the perspective of information theory by the information bottleneck (IB), Cramer-Rao lower bound, and Fisher information matrix. This paper makes two main contributions. First, we introduce a new upper bound for the standard information bottleneck relation, which we refer to as modified-IB in this paper. This measure is used to evaluate the performance of the SP algorithm in different sparsity levels and various amounts of noise. The MNIST, Fashion-MNIST and NYC-Taxi datasets were fed to the SP algorithm separately. The SP algorithm with learning was found to be resistant to noise. Adding up to 40% noise to the input resulted in no discernible change in the output. Using the probabilistic mapping method and Hidden Markov Model, the sparse SP output representation was reconstructed in the input space. In the modified-IB relation, it is numerically calculated that a lower noise level and a higher sparsity level in the SP algorithm lead to a more effective reconstruction and SP with 2% sparsity produces the best results. Our second contribution is to prove mathematically that more sparsity leads to better performance of the SP algorithm. The data distribution was considered the Cauchy distribution, and the Cramer-Rao lower bound was analyzed to estimate SP's output at different sparsity levels.
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Affiliation(s)
- Shiva Sanati
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Modjtaba Rouhani
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Ghosheh Abed Hodtani
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
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12
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Fakhar K, Dixit S, Hadaeghi F, Kording KP, Hilgetag CC. When Neural Activity Fails to Reveal Causal Contributions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543895. [PMID: 37333375 PMCID: PMC10274733 DOI: 10.1101/2023.06.06.543895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neuronal networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.
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Affiliation(s)
- Kayson Fakhar
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany
| | - Shrey Dixit
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany
| | - Fatemeh Hadaeghi
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany
| | - Konrad P. Kording
- Departments of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Learning in Machines & Brains, CIFAR, Toronto, ON, Canada
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany
- Department of Health Sciences, Boston University, Boston, MA, USA
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Wang XJ, Jiang J, Pereira-Obilinovic U. Bifurcation in space: Emergence of function modularity in the neocortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.04.543639. [PMID: 37333347 PMCID: PMC10274618 DOI: 10.1101/2023.06.04.543639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
How does functional modularity emerge in a multiregional cortex made with repeats of a canonical local circuit architecture? We investigated this question by focusing on neural coding of working memory, a core cognitive function. Here we report a mechanism dubbed "bifurcation in space", and show that its salient signature is spatially localized "critical slowing down" leading to an inverted V-shaped profile of neuronal time constants along the cortical hierarchy during working memory. The phenomenon is confirmed in connectome-based large-scale models of mouse and monkey cortices, offering an experimentally testable prediction to assess whether working memory representation is modular. Many bifurcations in space could explain the emergence of different activity patterns potentially deployed for distinct cognitive functions, This work demonstrates that a distributed mental representation is compatible with functional specificity as a consequence of macroscopic gradients of neurobiological properties across the cortex, suggesting a general principle for understanding brain's modular organization.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, 4 Washington Place, New York 10003, USA
| | - Junjie Jiang
- Center for Neural Science, New York University, 4 Washington Place, New York 10003, USA
- Present address: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Research Center for Brain-inspired Intelligence, Xi’an Jiaotong University, No.28, West Xianning Road, Xi’an, 710049, Shaanxi, P. R. China
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14
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Gao L, Liu S, Wang Y, Wu Q, Gou L, Yan J. Single-neuron analysis of dendrites and axons reveals the network organization in mouse prefrontal cortex. Nat Neurosci 2023:10.1038/s41593-023-01339-y. [PMID: 37217724 DOI: 10.1038/s41593-023-01339-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/18/2023] [Indexed: 05/24/2023]
Abstract
The structures of dendrites and axons form the basis for the connectivity of neural network, but their precise relationship at single-neuron level remains unclear. Here we report the complete dendrite and axon morphology of nearly 2,000 neurons in mouse prefrontal cortex (PFC). We identified morphological variations of somata, dendrites and axons across laminar layers and PFC subregions and the general rules of somatodendritic scaling with cytoarchitecture. We uncovered 24 morphologically distinguishable dendrite subtypes in 1,515 pyramidal projection neurons and 405 atypical pyramidal projection neurons and spiny stellate neurons with unique axon projection patterns. Furthermore, correspondence analysis among dendrites, local axons and long-range axons revealed coherent morphological changes associated with electrophysiological phenotypes. Finally, integrative dendrite-axon analysis uncovered the organization of potential intra-column, inter-hemispheric and inter-column connectivity among projection neuron types in PFC. Together, our study provides a comprehensive structural repertoire for the reconstruction and analysis of PFC neural network.
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Affiliation(s)
- Le Gao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Sang Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Shanghai, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yanzhi Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Shanghai, China
| | - Qiwen Wu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Shanghai, China
| | - Lingfeng Gou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jun Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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15
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Pessoa L. Disentangling Some Conceptual Knots. J Cogn Neurosci 2023; 35:391-395. [PMID: 36626350 PMCID: PMC11019943 DOI: 10.1162/jocn_a_01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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16
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Sejnowski TJ. Large Language Models and the Reverse Turing Test. Neural Comput 2023; 35:309-342. [PMID: 36746144 PMCID: PMC10177005 DOI: 10.1162/neco_a_01563] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/21/2022] [Indexed: 02/08/2023]
Abstract
Large language models (LLMs) have been transformative. They are pretrained foundational models that are self-supervised and can be adapted with fine-tuning to a wide range of natural language tasks, each of which previously would have required a separate network model. This is one step closer to the extraordinary versatility of human language. GPT-3 and, more recently, LaMDA, both of them LLMs, can carry on dialogs with humans on many topics after minimal priming with a few examples. However, there has been a wide range of reactions and debate on whether these LLMs understand what they are saying or exhibit signs of intelligence. This high variance is exhibited in three interviews with LLMs reaching wildly different conclusions. A new possibility was uncovered that could explain this divergence. What appears to be intelligence in LLMs may in fact be a mirror that reflects the intelligence of the interviewer, a remarkable twist that could be considered a reverse Turing test. If so, then by studying interviews, we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs. As LLMs become more capable, they may transform the way we interact with machines and how they interact with each other. Increasingly, LLMs are being coupled with sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A road map for achieving artificial general autonomy is outlined with seven major improvements inspired by brain systems and how LLMs could in turn be used to uncover new insights into brain function.
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Affiliation(s)
- Terrence J Sejnowski
- Salk Institute for Biological Studies, La Jolla, CA 92093, U.S.A.,Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92037, U.S.A.
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17
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van den Brink RL, Hagena K, Wilming N, Murphy PR, Büchel C, Donner TH. Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain. Neuron 2023; 111:571-584.e9. [PMID: 36476977 DOI: 10.1016/j.neuron.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.
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Affiliation(s)
- Ruud L van den Brink
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Keno Hagena
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Niklas Wilming
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Peter R Murphy
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland; Department of Psychology, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Christian Büchel
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Tobias H Donner
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
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18
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Krull KR. Risk factors and screening for neurocognitive impacts of therapy. HEMATOLOGY. AMERICAN SOCIETY OF HEMATOLOGY. EDUCATION PROGRAM 2022; 2022:259-265. [PMID: 36485082 PMCID: PMC9821256 DOI: 10.1182/hematology.2022000409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Long-term survivors of pediatric hematologic malignancies are at elevated risk for neurocognitive impairment. Such impairment manifests in different ways at different times during survivorship, with deficits in processing speed, attention, and memory often appearing before deficits in executive function, intelligence, and academics. Survivors exposed to therapies that directly target the central nervous system (CNS), as is the case in acute lymphoblastic leukemia, may demonstrate subtle deficits during frontline therapy, and these deficits may grow and evolve over time. Survivors who do not receive CNS-directed therapies (eg, Hodgkin lymphoma) are also at elevated risk for neurocognitive impairment, although the influence on brain function is indirect through cancer therapy impact on systemic organ function vital to brain health (eg, cardiopulmonary morbidity). Over the course of the survivor's life span, the presence and impact of neurocognitive deficits will be determined by a complex interaction between premorbid development and environment, cancer therapy and clinical care, and posttreatment recovery and health. The timing and type of these treatment and health events will dictate the approach to screening and monitoring for neurocognitive impairment.
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Affiliation(s)
- Kevin R. Krull
- Correspondence Kevin R. Krull, 262 Danny Thomas Place, MS 735, Memphis, TN 38105-3678, USA; e-mail:
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19
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Tesileanu T, Piasini E, Balasubramanian V. Efficient processing of natural scenes in visual cortex. Front Cell Neurosci 2022; 16:1006703. [PMID: 36545653 PMCID: PMC9760692 DOI: 10.3389/fncel.2022.1006703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.
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Affiliation(s)
- Tiberiu Tesileanu
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, United States,*Correspondence: Tiberiu Tesileanu
| | - Eugenio Piasini
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy,Eugenio Piasini
| | - Vijay Balasubramanian
- Department of Physics and Astronomy, David Rittenhouse Laboratory, University of Pennsylvania, Philadelphia, PA, United States,Santa Fe Institute, Santa Fe, NM, United States
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