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Bharmauria V, Ramezanpour H, Ouelhazi A, Yahia Belkacemi Y, Flouty O, Molotchnikoff S. KETAMINE: Neural- and network-level changes. Neuroscience 2024; 559:188-198. [PMID: 39245312 DOI: 10.1016/j.neuroscience.2024.09.010] [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: 07/08/2024] [Revised: 08/30/2024] [Accepted: 09/03/2024] [Indexed: 09/10/2024]
Abstract
Ketamine is a widely used clinical drug that has several functional and clinical applications, including its use as an anaesthetic, analgesic, anti-depressive, anti-suicidal agent, among others. Among its diverse behavioral effects, it influences short-term memory and induces psychedelic effects. At the neural level across different brain areas, it modulates neural firing rates, neural tuning, brain oscillations, and modularity, while promoting hypersynchrony and random connectivity between neurons. In our recent studies we demonstrated that topical application of ketamine on the visual cortex alters neural tuning and promotes vigorous connectivity between neurons by decreasing their firing variability. Here, we begin with a brief review of the literature, followed by results from our lab, where we synthesize a dendritic model of neural tuning and network changes following ketamine application. This model has potential implications for focused modulation of cortical networks in clinical settings. Finally, we identify current gaps in research and suggest directions for future studies, particularly emphasizing the need for more animal experiments to establish a platform for effective translation and synergistic therapies combining ketamine with other protocols such as training and adaptation. In summary, investigating ketamine's broader systemic effects, not only provides deeper insight into cognitive functions and consciousness but also paves the way to advance therapies for neuropsychiatric disorders.
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Affiliation(s)
- Vishal Bharmauria
- The Tampa Human Neurophysiology Lab & Department of Neurosurgery and Brain Repair, Morsani College of Medicine, 2 Tampa General Circle, University of South Florida, Tampa, FL 33606, USA; Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada.
| | - Hamidreza Ramezanpour
- Department of Biology, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada
| | - Afef Ouelhazi
- Neurophysiology of the Visual system, Département de Sciences Biologiques, 1375 Av. Thérèse-Lavoie-Roux, Université de Montréal, Montréal, Québec H2V 0B3, Canada
| | - Yassine Yahia Belkacemi
- Neurophysiology of the Visual system, Département de Sciences Biologiques, 1375 Av. Thérèse-Lavoie-Roux, Université de Montréal, Montréal, Québec H2V 0B3, Canada
| | - Oliver Flouty
- The Tampa Human Neurophysiology Lab & Department of Neurosurgery and Brain Repair, Morsani College of Medicine, 2 Tampa General Circle, University of South Florida, Tampa, FL 33606, USA
| | - Stéphane Molotchnikoff
- Neurophysiology of the Visual system, Département de Sciences Biologiques, 1375 Av. Thérèse-Lavoie-Roux, Université de Montréal, Montréal, Québec H2V 0B3, Canada
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2
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Zhu S, Xia R, Chen X, Moore T. Comparison of orientation encoding across layers within single columns of primate V1 revealed by high-density recordings. Front Neural Circuits 2024; 18:1399571. [PMID: 39377033 PMCID: PMC11456443 DOI: 10.3389/fncir.2024.1399571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 09/09/2024] [Indexed: 10/09/2024] Open
Abstract
Primary visual cortex (V1) has been the focus of extensive neurophysiological investigations, with its laminar organization serving as a crucial model for understanding the functional logic of neocortical microcircuits. Utilizing newly developed high-density, Neuropixels probes, we measured visual responses from large populations of simultaneously recorded neurons distributed across layers of macaque V1. Within single recordings, myriad differences in the functional properties of neuronal subpopulations could be observed. Notably, while standard measurements of orientation selectivity showed only minor differences between laminar compartments, decoding stimulus orientation from layer 4C responses outperformed both superficial and deep layers within the same cortical column. The superior orientation discrimination within layer 4C was associated with greater response reliability of individual neurons rather than lower correlated activity within neuronal populations. Our results underscore the efficacy of high-density electrophysiology in revealing the functional organization and network properties of neocortical microcircuits within single experiments.
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Affiliation(s)
- Shude Zhu
- Department of Neurobiology and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, United States
| | - Ruobing Xia
- Department of Neurobiology and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, United States
| | - Xiaomo Chen
- Department of Neurobiology, Physiology, and Behavior, Center for Neuroscience, UC Davis, Davis, CA, United States
| | - Tirin Moore
- Department of Neurobiology and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, United States
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3
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [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] [Indexed: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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4
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Jáidar O, Albarran E, Albarran EN, Wu YW, Ding JB. Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.596654. [PMID: 38895486 PMCID: PMC11185645 DOI: 10.1101/2024.06.06.596654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The striatum is required for normal action selection, movement, and sensorimotor learning. Although action-specific striatal ensembles have been well documented, it is not well understood how these ensembles are formed and how their dynamics may evolve throughout motor learning. Here we used longitudinal 2-photon Ca2+ imaging of dorsal striatal neurons in head-fixed mice as they learned to self-generate locomotion. We observed a significant activation of both direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs, respectively) during early locomotion bouts and sessions that gradually decreased over time. For dSPNs, onset- and offset-ensembles were gradually refined from active motion-nonspecific cells. iSPN ensembles emerged from neurons initially active during opponent actions before becoming onset- or offset-specific. Our results show that as striatal ensembles are progressively refined, the number of active nonspecific striatal neurons decrease and the overall efficiency of the striatum information encoding for learned actions increases.
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Affiliation(s)
- Omar Jáidar
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Eddy Albarran
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Current address: Columbia University
| | | | - Yu-Wei Wu
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Current address: Institute of Molecular Biology, Academia Sinica
| | - Jun B. Ding
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
- The Phil & Penny Knight Initiative for Brain Resilience at the Wu Tsai Neurosciences Institute, Stanford University
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5
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Wang T, Dai W, Wu Y, Li Y, Yang Y, Zhang Y, Zhou T, Sun X, Wang G, Li L, Dou F, Xing D. Nonuniform and pathway-specific laminar processing of spatial frequencies in the primary visual cortex of primates. Nat Commun 2024; 15:4005. [PMID: 38740786 DOI: 10.1038/s41467-024-48379-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
The neocortex comprises six cortical layers that play a crucial role in information processing; however, it remains unclear whether laminar processing is consistent across all regions within a single cortex. In this study, we demonstrate diverse laminar response patterns in the primary visual cortex (V1) of three male macaque monkeys when exposed to visual stimuli at different spatial frequencies (SFs). These response patterns can be categorized into two groups. One group exhibit suppressed responses in the output layers for all SFs, while the other type shows amplified responses specifically at high SFs. Further analysis suggests that both magnocellular (M) and parvocellular (P) pathways contribute to the suppressive effect through feedforward mechanisms, whereas amplification is specific to local recurrent mechanisms within the parvocellular pathway. These findings highlight the non-uniform distribution of neural mechanisms involved in laminar processing and emphasize how pathway-specific amplification selectively enhances representations of high-SF information in primate V1.
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Affiliation(s)
- Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- College of Life Sciences, Beijing Normal University, Beijing, 100875, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yange Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Tingting Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xiaowen Sun
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Gang Wang
- Beijing Institute of Basic Medical Sciences, Beijing, 100005, China
| | - Liang Li
- Beijing Institute of Basic Medical Sciences, Beijing, 100005, China
| | - Fei Dou
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- College of Life Sciences, Beijing Normal University, Beijing, 100875, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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6
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Gelens F, Äijälä J, Roberts L, Komatsu M, Uran C, Jensen MA, Miller KJ, Ince RAA, Garagnani M, Vinck M, Canales-Johnson A. Distributed representations of prediction error signals across the cortical hierarchy are synergistic. Nat Commun 2024; 15:3941. [PMID: 38729937 PMCID: PMC11087548 DOI: 10.1038/s41467-024-48329-7] [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: 07/12/2023] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.
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Affiliation(s)
- Frank Gelens
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Juho Äijälä
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Louis Roberts
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
- Department of Computing, Goldsmiths, University of London, SE14 6NW, London, UK
| | - Misako Komatsu
- Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Brain Science Institute, Saitama, 351-0198, Japan
| | - Cem Uran
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
- Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525, Nijmegen, The Netherlands
| | - Michael A Jensen
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Robin A A Ince
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, G12 8QB, Scotland, UK
| | - Max Garagnani
- Department of Computing, Goldsmiths, University of London, SE14 6NW, London, UK
- Brain Language Lab, Freie Universität Berlin, 14195, Berlin, Germany
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany.
- Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525, Nijmegen, The Netherlands.
| | - Andres Canales-Johnson
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK.
- Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, 3460000, Talca, Chile.
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7
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [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: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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8
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O'Reilly D, Delis I. Dissecting muscle synergies in the task space. eLife 2024; 12:RP87651. [PMID: 38407224 PMCID: PMC10942626 DOI: 10.7554/elife.87651] [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] [Indexed: 02/27/2024] Open
Abstract
The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles 'working together' towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can 'work together' in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.
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Affiliation(s)
- David O'Reilly
- School of Biomedical Sciences, University of LeedsLeedsUnited Kingdom
| | - Ioannis Delis
- School of Biomedical Sciences, University of LeedsLeedsUnited Kingdom
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9
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Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex. Brain Inform 2023; 10:34. [PMID: 38052917 PMCID: PMC10697912 DOI: 10.1186/s40708-023-00212-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
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Affiliation(s)
- Loren Koçillari
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
| | - Marco Celotto
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany
- Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy
| | - Nikolas A Francis
- Department of Biology and Brain and Behavior Institute, University of Maryland, College Park, MD, 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
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10
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Rowe EG, Zhang Y, Garrido MI. Evidence for adaptive myelination of subcortical shortcuts for visual motion perception in healthy adults. Hum Brain Mapp 2023; 44:5641-5654. [PMID: 37608684 PMCID: PMC10619379 DOI: 10.1002/hbm.26467] [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: 02/15/2023] [Revised: 05/27/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023] Open
Abstract
Conscious visual motion information follows a cortical pathway from the retina to the lateral geniculate nucleus (LGN) and on to the primary visual cortex (V1) before arriving at the middle temporal visual area (MT/V5). Alternative subcortical pathways that bypass V1 are thought to convey unconscious visual information. One flows from the retina to the pulvinar (PUL) and on to medial temporal visual area (MT); while the other directly connects the LGN to MT. Evidence for these pathways comes from non-human primates and modest-sized studies in humans with brain lesions. Thus, the aim of the current study was to reconstruct these pathways in a large sample of neurotypical individuals and to determine the degree to which these pathways are myelinated, suggesting information flow is rapid. We used the publicly available 7T (N = 98; 'discovery') and 3T (N = 381; 'validation') diffusion magnetic resonance imaging datasets from the Human Connectome Project to reconstruct the PUL-MT (including all subcompartments of the PUL) and LGN-MT pathways. We found more fibre tracts with greater density in the left hemisphere. Although the left PUL-MT path was denser, the bilateral LGN-MT tracts were more heavily myelinated, suggesting faster signal transduction. We suggest that this apparent discrepancy may be due to 'adaptive myelination' caused by more frequent use of the LGN-MT pathway that leads to greater myelination and faster overall signal transmission.
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Affiliation(s)
- Elise G. Rowe
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yubing Zhang
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Marta I. Garrido
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Graeme Clark Institute for Biomedical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
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11
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Andrei AR, Akil AE, Kharas N, Rosenbaum R, Josić K, Dragoi V. Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo. Nat Neurosci 2023; 26:1960-1969. [PMID: 37828225 DOI: 10.1038/s41593-023-01446-w] [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: 03/10/2022] [Accepted: 09/01/2023] [Indexed: 10/14/2023]
Abstract
To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.
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Affiliation(s)
- Ariana R Andrei
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA.
| | - Alan E Akil
- Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Natasha Kharas
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Krešimir Josić
- Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
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12
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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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13
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Celeghin A, Borriero A, Orsenigo D, Diano M, Méndez Guerrero CA, Perotti A, Petri G, Tamietto M. Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues. Front Comput Neurosci 2023; 17:1153572. [PMID: 37485400 PMCID: PMC10359983 DOI: 10.3389/fncom.2023.1153572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open
Abstract
Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as in silico models of the primate visual system. Specifically, we highlight several emerging notions about the anatomical and physiological properties of the visual system that still need to be systematically integrated into current CNN models. These tenets include the implementation of parallel processing pathways from the early stages of retinal input and the reconsideration of several assumptions concerning the serial progression of information flow. We suggest design choices and architectural constraints that could facilitate a closer alignment with biology provide causal evidence of the predictive link between the artificial and biological visual systems. Adopting this principled perspective could potentially lead to new research questions and applications of CNNs beyond modeling object recognition.
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Affiliation(s)
| | | | - Davide Orsenigo
- Department of Psychology, University of Torino, Turin, Italy
| | - Matteo Diano
- Department of Psychology, University of Torino, Turin, Italy
| | | | | | | | - Marco Tamietto
- Department of Psychology, University of Torino, Turin, Italy
- Department of Medical and Clinical Psychology, and CoRPS–Center of Research on Psychology in Somatic Diseases–Tilburg University, Tilburg, Netherlands
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14
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Beshkar M. The QBIT Theory: Consciousness from Entangled Qubits. Integr Psychol Behav Sci 2022:10.1007/s12124-022-09745-w. [PMID: 36567412 DOI: 10.1007/s12124-022-09745-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2022] [Indexed: 12/26/2022]
Abstract
How does the brain generate consciousness? The present paper is an attempt to answer this question from the perspective of the QBIT theory. In sum, the theory argues that the brain has a prior belief (P) about the stimulus that has caused a sensory representation (R) to be created in the brain. When the conditional entropy of P given R becomes less than zero, the brain becomes more than certain about (i.e. becomes conscious of) the stimulus. Conditional entropy can become negative (and thus the brain can become more than certain) only if the brain uses entangled quantum information in its computations. The QBIT theory suggests that, at the most fundamental level, consciousness is nothing but a special kind of entangled information.
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Affiliation(s)
- Majid Beshkar
- Tehran University of Medical Sciences, Tehran, Iran.
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15
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Newman EL, Varley TF, Parakkattu VK, Sherrill SP, Beggs JM. Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. ENTROPY (BASEL, SWITZERLAND) 2022; 24:930. [PMID: 35885153 PMCID: PMC9319160 DOI: 10.3390/e24070930] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022]
Abstract
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the "higher-order" information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure-function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.
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Affiliation(s)
- Ehren L. Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | - Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | - Vibin K. Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | | | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, IN 47405, USA;
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16
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Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci 2022; 23:551-567. [PMID: 35732917 DOI: 10.1038/s41583-022-00606-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 12/17/2022]
Abstract
The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.
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Affiliation(s)
- Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany. .,Istituto Italiano di Tecnologia, Rovereto, Italy.
| | | | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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17
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Rockland KS. Clustered Intrinsic Connections: Not a Single System. Front Syst Neurosci 2022; 16:910845. [PMID: 35720440 PMCID: PMC9203679 DOI: 10.3389/fnsys.2022.910845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/16/2022] [Indexed: 12/18/2022] Open
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18
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Francis NA, Mukherjee S, Koçillari L, Panzeri S, Babadi B, Kanold PO. Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep 2022; 39:110878. [PMID: 35649366 PMCID: PMC9387204 DOI: 10.1016/j.celrep.2022.110878] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/28/2022] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
Cortical processing of task-relevant information enables recognition of behaviorally meaningful sensory events. It is unclear how task-related information is represented within cortical networks by the activity of individual neurons and their functional interactions. Here, we use two-photon imaging to record neuronal activity from the primary auditory cortex of mice during a pure-tone discrimination task. We find that a subset of neurons transiently encode sensory information used to inform behavioral choice. Using Granger causality analysis, we show that these neurons form functional networks in which information transmits sequentially. Network structures differ for target versus non-target tones, encode behavioral choice, and differ between correct versus incorrect behavioral choices. Correct behavioral choices are associated with shorter communication timescales, larger functional correlations, and greater information redundancy. In summary, specialized neurons in primary auditory cortex integrate task-related information and form functional networks whose structures encode both sensory input and behavioral choice. Francis et al. find that, as mice perform an auditory discrimination task, cortical neurons form functional networks in which task-relevant information transmits sequentially between neurons. Network structures encode behavioral choice, and correct behavioral choices are associated with shorter communication timescales, larger functional correlations, and greater information redundancy between neurons.
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Affiliation(s)
- Nikolas A Francis
- Department of Biology & Brain and Behavior Institute, University of Maryland, College Park, MD 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering & Institute for Systems Research, University of Maryland, College Park, MD 20742, USA
| | - Loren Koçillari
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Rovereto 38068, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Stefano Panzeri
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Rovereto 38068, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany.
| | - Behtash Babadi
- Department of Electrical and Computer Engineering & Institute for Systems Research, University of Maryland, College Park, MD 20742, USA.
| | - Patrick O Kanold
- Department of Biology & Brain and Behavior Institute, University of Maryland, College Park, MD 20742, USA; Department of Biomedical Engineering & Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205, USA.
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19
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Theiss JD, Bowen JD, Silver MA. Spatial Attention Enhances Crowded Stimulus Encoding Across Modeled Receptive Fields by Increasing Redundancy of Feature Representations. Neural Comput 2021; 34:190-218. [PMID: 34710898 PMCID: PMC8693207 DOI: 10.1162/neco_a_01447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 07/01/2021] [Indexed: 11/04/2022]
Abstract
Any visual system, biological or artificial, must make a trade-off between the number of units used to represent the visual environment and the spatial resolution of the sampling array. Humans and some other animals are able to allocate attention to spatial locations to reconfigure the sampling array of receptive fields (RFs), thereby enhancing the spatial resolution of representations without changing the overall number of sampling units. Here, we examine how representations of visual features in a fully convolutional neural network interact and interfere with each other in an eccentricity-dependent RF pooling array and how these interactions are influenced by dynamic changes in spatial resolution across the array. We study these feature interactions within the framework of visual crowding, a well-characterized perceptual phenomenon in which target objects in the visual periphery that are easily identified in isolation are much more difficult to identify when flanked by similar nearby objects. By separately simulating effects of spatial attention on RF size and on the density of the pooling array, we demonstrate that the increase in RF density due to attention is more beneficial than changes in RF size for enhancing target classification for crowded stimuli. Furthermore, by varying target/flanker spacing, as well as the spatial extent of attention, we find that feature redundancy across RFs has more influence on target classification than the fidelity of the feature representations themselves. Based on these findings, we propose a candidate mechanism by which spatial attention relieves visual crowding through enhanced feature redundancy that is mostly due to increased RF density.
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Affiliation(s)
| | - Joel D Bowen
- University of California, Berkeley, CA 94720, U.S.A.
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20
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Andrei AR, Debes S, Chelaru M, Liu X, Rodarte E, Spudich JL, Janz R, Dragoi V. Heterogeneous side effects of cortical inactivation in behaving animals. eLife 2021; 10:66400. [PMID: 34505577 PMCID: PMC8457825 DOI: 10.7554/elife.66400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 09/09/2021] [Indexed: 12/05/2022] Open
Abstract
Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.
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Affiliation(s)
- Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Samantha Debes
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Mircea Chelaru
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Xiaoqin Liu
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Elsa Rodarte
- Department of Neurology, McGovern Medical School, University of Texas, Houston, United States
| | - John L Spudich
- Center for Membrane Biology, Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas, Houston, United States
| | - Roger Janz
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
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21
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Azeredo da Silveira R, Rieke F. The Geometry of Information Coding in Correlated Neural Populations. Annu Rev Neurosci 2021; 44:403-424. [PMID: 33863252 DOI: 10.1146/annurev-neuro-120320-082744] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.
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Affiliation(s)
| | - Fred Rieke
- Department of Physics, Ecole Normale Supérieure, 75005 Paris, France;
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22
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Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex. Cell Rep 2020; 33:108367. [PMID: 33176154 DOI: 10.1016/j.celrep.2020.108367] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 12/28/2019] [Accepted: 10/19/2020] [Indexed: 11/22/2022] Open
Abstract
In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match-to-sample tasks on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while being recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decode correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons into most informative and less informative and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled, and correlated than less informative neurons. Because neurons are divided into two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling, and correlations within the coding pool are stronger than across coding pools.
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23
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Tauste Campo A. Inferring neural information flow from spiking data. Comput Struct Biotechnol J 2020; 18:2699-2708. [PMID: 33101608 PMCID: PMC7548302 DOI: 10.1016/j.csbj.2020.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 01/02/2023] Open
Abstract
The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.
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Affiliation(s)
- Adrià Tauste Campo
- Centre for Brain and Cognition, Universitat Pompeu Fabra, Ramon Trias Fargas 25, 08018 Barcelona, Spain
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24
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Sherrill SP, Timme NM, Beggs JM, Newman EL. Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales. Netw Neurosci 2020; 4:678-697. [PMID: 32885121 PMCID: PMC7462423 DOI: 10.1162/netn_a_00141] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/06/2020] [Indexed: 11/19/2022] Open
Abstract
Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05-14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission-of which synergistic processing is a component-that resulted as mutual information grew. However, at extrasynaptic windows (up to 3,000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high-correlation regime, synergistic processing decreases with greater correlation.
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Affiliation(s)
- Samantha P. Sherrill
- Department of Psychological and Brain Sciences and Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA
| | - Nicholas M. Timme
- Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - John M. Beggs
- Department of Physics & Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA
| | - Ehren L. Newman
- Department of Psychological and Brain Sciences and Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA
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25
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Abstract
A dot in this text can simultaneously stimulate thousands of neurons in primary visual cortex, a response that may seem unnecessarily redundant. Contrary to such a view, in this issue of Neuron, Nigam et al. (2019) demonstrate that these co-activated neurons generate abundant synergistic interactions that help to decode the stimulus.
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Affiliation(s)
- Reece Mazade
- State University of New York, College of Optometry, New York, NY 10036, USA
| | - Jose Manuel Alonso
- State University of New York, College of Optometry, New York, NY 10036, USA.
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