1
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Holt CJ, Miller KD, Ahmadian Y. The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations. PLoS Comput Biol 2024; 20:e1012190. [PMID: 38935792 DOI: 10.1371/journal.pcbi.1012190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 05/23/2024] [Indexed: 06/29/2024] Open
Abstract
When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.
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Affiliation(s)
- Caleb J Holt
- Department of Physics, Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America
| | - Kenneth D Miller
- Deptartment of Neuroscience, Center for Theoretical Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, and Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - Yashar Ahmadian
- Department of Engineering, Computational and Biological Learning Lab, University of Cambridge, Cambridge, United Kingdom
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2
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LaFosse PK, Zhou Z, O'Rawe JF, Friedman NG, Scott VM, Deng Y, Histed MH. Single-cell optogenetics reveals attenuation-by-suppression in visual cortical neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.13.557650. [PMID: 37745464 PMCID: PMC10515908 DOI: 10.1101/2023.09.13.557650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cells' input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons' activity varies with sensory stimuli. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons' IO functions can filter sensory inputs - not only do sensory stimuli change neurons' spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged.
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Affiliation(s)
- Paul K LaFosse
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
- NIH-University of Maryland Graduate Partnerships Program, Bethesda, MD USA 20892
- Neuroscience and Cognitive Science Program, University of Maryland College Park, College Park, MD USA 20742
| | - Zhishang Zhou
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
| | - Jonathan F O'Rawe
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
| | - Nina G Friedman
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
- NIH-University of Maryland Graduate Partnerships Program, Bethesda, MD USA 20892
- Neuroscience and Cognitive Science Program, University of Maryland College Park, College Park, MD USA 20742
| | - Victoria M Scott
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
| | - Yanting Deng
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
| | - Mark H Histed
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA 20892
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3
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Desowska A, Coffman S, Kim I, Underwood E, Tao A, Lopez KL, Nelson CA, Hensch TK, Gabard-Durnam L, Cornelissen L, Berde CB. Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old. Neuroimage Clin 2024; 42:103614. [PMID: 38754325 PMCID: PMC11126539 DOI: 10.1016/j.nicl.2024.103614] [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: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. METHODS EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. RESULTS Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures. CONCLUSIONS We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Siobhan Coffman
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Isabelle Kim
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Ellen Underwood
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Alice Tao
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kelsie L Lopez
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Charles A Nelson
- Laboratories of Cognitive Neurosciences, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Takao K Hensch
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | | | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA.
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4
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Politi A, Torcini A. A robust balancing mechanism for spiking neural networks. CHAOS (WOODBURY, N.Y.) 2024; 34:041102. [PMID: 38639569 DOI: 10.1063/5.0199298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 04/20/2024]
Abstract
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.
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Affiliation(s)
- Antonio Politi
- Institute for Complex Systems and Mathematical Biology and Department of Physics, Aberdeen AB24 3UE, United Kingdom
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
| | - Alessandro Torcini
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
- Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS UMR 8089, 95302 Cergy-Pontoise cedex, France
- INFN Sezione di Firenze, Via Sansone 1 50019 Sesto Fiorentino, Italy
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5
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Goris RLT, Coen-Cagli R, Miller KD, Priebe NJ, Lengyel M. Response sub-additivity and variability quenching in visual cortex. Nat Rev Neurosci 2024; 25:237-252. [PMID: 38374462 DOI: 10.1038/s41583-024-00795-0] [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] [Accepted: 01/24/2024] [Indexed: 02/21/2024]
Abstract
Sub-additivity and variability are ubiquitous response motifs in the primary visual cortex (V1). Response sub-additivity enables the construction of useful interpretations of the visual environment, whereas response variability indicates the factors that limit the precision with which the brain can do this. There is increasing evidence that experimental manipulations that elicit response sub-additivity often also quench response variability. Here, we provide an overview of these phenomena and suggest that they may have common origins. We discuss empirical findings and recent model-based insights into the functional operations, computational objectives and circuit mechanisms underlying V1 activity. These different modelling approaches all predict that response sub-additivity and variability quenching often co-occur. The phenomenology of these two response motifs, as well as many of the insights obtained about them in V1, generalize to other cortical areas. Thus, the connection between response sub-additivity and variability quenching may be a canonical motif across the cortex.
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Affiliation(s)
- Robbe L T Goris
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Dept. of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Swartz Program in Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Nicholas J Priebe
- Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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6
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Pattadkal JJ, Zemelman BV, Fiete I, Priebe NJ. Primate neocortex performs balanced sensory amplification. Neuron 2024; 112:661-675.e7. [PMID: 38091984 PMCID: PMC10922204 DOI: 10.1016/j.neuron.2023.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/08/2023] [Accepted: 11/07/2023] [Indexed: 01/25/2024]
Abstract
The sensory cortex amplifies relevant features of external stimuli. This sensitivity and selectivity arise through the transformation of inputs by cortical circuitry. We characterize the circuit mechanisms and dynamics of cortical amplification by making large-scale simultaneous measurements of single cells in awake primates and testing computational models. By comparing network activity in both driven and spontaneous states with models, we identify the circuit as operating in a regime of non-normal balanced amplification. Incoming inputs are strongly but transiently amplified by strong recurrent feedback from the disruption of excitatory-inhibitory balance in the network. Strong inhibition rapidly quenches responses, thereby permitting the tracking of time-varying stimuli.
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Affiliation(s)
- Jagruti J Pattadkal
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Boris V Zemelman
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ila Fiete
- Department of Brain and Cognitive Sciences, MIT, Boston, MA 02139, USA
| | - Nicholas J Priebe
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA.
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7
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Holt CJ, Miller KD, Ahmadian Y. The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540442. [PMID: 37214812 PMCID: PMC10197697 DOI: 10.1101/2023.05.11.540442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.
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Affiliation(s)
- Caleb J Holt
- Institute of Neuroscience, Department of Physics, University of Oregon, OR, USA
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, and Dept. of Neuroscience, College of Physicians and Surgeons and Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY, USA
| | - Yashar Ahmadian
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
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8
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Parida S, Liu ST, Sadagopan S. Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model. Commun Biol 2023; 6:456. [PMID: 37130918 PMCID: PMC10154343 DOI: 10.1038/s42003-023-04816-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 04/05/2023] [Indexed: 05/04/2023] Open
Abstract
For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated using guinea pig and marmoset vocalizations that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or more adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.
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Affiliation(s)
- Satyabrata Parida
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shi Tong Liu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Srivatsun Sadagopan
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, USA.
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9
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Zhu RJB, Wei XX. Unsupervised approach to decomposing neural tuning variability. Nat Commun 2023; 14:2298. [PMID: 37085524 PMCID: PMC10121715 DOI: 10.1038/s41467-023-37982-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.
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Affiliation(s)
- Rong J B Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China.
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin, Austin, USA.
- Department of Psychology, The University of Texas at Austin, Austin, USA.
- Center for Perceptual Systems, The University of Texas at Austin, Austin, USA.
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, USA.
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10
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Liu W, Liu X. Pre-stimulus network responses affect information coding in neural variability quenching. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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11
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Mosheiff N, Ermentrout B, Huang C. Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability. PLoS Comput Biol 2023; 19:e1010843. [PMID: 36626362 PMCID: PMC9870129 DOI: 10.1371/journal.pcbi.1010843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/23/2023] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.
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Affiliation(s)
- Noga Mosheiff
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
| | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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12
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Shapiro JT, Gosselin EAR, Michaud NM, Crowder NA. Activating parvalbumin-expressing interneurons produces iceberg effects in mouse primary visual cortex neurons. Neurosci Lett 2022; 786:136804. [PMID: 35843471 DOI: 10.1016/j.neulet.2022.136804] [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: 04/22/2022] [Revised: 07/12/2022] [Accepted: 07/12/2022] [Indexed: 10/17/2022]
Abstract
In the primary visual cortex (V1) inhibitory interneurons form a local circuit with excitatory pyramidal cells to produce distinct receptive field properties. Parvalbumin-expressing interneurons (Pvalb+) are the most common subclass of V1 interneurons, and studies of orientation tuning indicate they shape pyramidal stimulus selectivity by balancing excitation with inhibition relative to the spike threshold. The iceberg effect, where subthreshold responses have broader tuning than spiking responses, predicts that other receptive field properties besides orientation tuning should also be affected by this balance mediated by Pvalb+ cells. To test this, we measured receptive field size and visual latency of pyramidal cells while Pvalb+ activity was optogenetically increased. We found that amplifying Pvalb+ input to pyramidal cells significantly increased their latency and decreased their receptive field size, which corroborates the proposed role of Pvalb+ interneurons in sculpting pyramidal tuning by controlling cortical gain.
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Affiliation(s)
- Jared T Shapiro
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada
| | - Emily A R Gosselin
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada
| | - Nicole M Michaud
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada
| | - Nathan A Crowder
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
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13
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Domijan D, Marić M. A multi-scale neurodynamic implementation of incremental grouping. Vision Res 2022; 197:108057. [PMID: 35487147 DOI: 10.1016/j.visres.2022.108057] [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: 12/06/2021] [Revised: 03/25/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
Incremental grouping is a process entailing serial binding of distal image elements into a unified object representation. At the neural level, incremental grouping involves propagation of the enhanced firing rate among feature-tuned neurons in the early visual cortex. Here, we developed a multi-resolution neural model of incremental grouping. In the model, propagation of the enhanced firing rate is achieved by computing the activity difference between two sets of units: attentional or A-units, whose firing rate is modulated by their horizontal collaterals, and non-attentional or N-units that receive only feedforward input. The activity difference is computed on dendrites that act as independent computational subunits. The proposed model employs multiple spatial scales to account for a variable speed of incremental grouping. In addition, the model incorporates the L-junction detection network that enables incremental grouping over L-junctions. Computer simulations show that the timing of attentional modulations in the model is comparable with neurophysiological measurements in monkey primary visual cortex.
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14
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Auerbach BD, Gritton HJ. Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss. Front Neurosci 2022; 16:799787. [PMID: 35221899 PMCID: PMC8866963 DOI: 10.3389/fnins.2022.799787] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/18/2022] [Indexed: 12/12/2022] Open
Abstract
Listening in noisy or complex sound environments is difficult for individuals with normal hearing and can be a debilitating impairment for those with hearing loss. Extracting meaningful information from a complex acoustic environment requires the ability to accurately encode specific sound features under highly variable listening conditions and segregate distinct sound streams from multiple overlapping sources. The auditory system employs a variety of mechanisms to achieve this auditory scene analysis. First, neurons across levels of the auditory system exhibit compensatory adaptations to their gain and dynamic range in response to prevailing sound stimulus statistics in the environment. These adaptations allow for robust representations of sound features that are to a large degree invariant to the level of background noise. Second, listeners can selectively attend to a desired sound target in an environment with multiple sound sources. This selective auditory attention is another form of sensory gain control, enhancing the representation of an attended sound source while suppressing responses to unattended sounds. This review will examine both “bottom-up” gain alterations in response to changes in environmental sound statistics as well as “top-down” mechanisms that allow for selective extraction of specific sound features in a complex auditory scene. Finally, we will discuss how hearing loss interacts with these gain control mechanisms, and the adaptive and/or maladaptive perceptual consequences of this plasticity.
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Affiliation(s)
- Benjamin D. Auerbach
- Department of Molecular and Integrative Physiology, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- *Correspondence: Benjamin D. Auerbach,
| | - Howard J. Gritton
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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15
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Liu X, Robinson PA. Analytic Model for Feature Maps in the Primary Visual Cortex. Front Comput Neurosci 2022; 16:659316. [PMID: 35185503 PMCID: PMC8854373 DOI: 10.3389/fncom.2022.659316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 01/05/2022] [Indexed: 11/29/2022] Open
Abstract
A compact analytic model is proposed to describe the combined orientation preference (OP) and ocular dominance (OD) features of simple cells and their mutual constraints on the spatial layout of the combined OP-OD map in the primary visual cortex (V1). This model consists of three parts: (i) an anisotropic Laplacian (AL) operator that represents the local neural sensitivity to the orientation of visual inputs; and (ii) obtain a receptive field (RF) operator that models the anisotropic spatial projection from nearby neurons to a given V1 cell over scales of a few tenths of a millimeter and combines with the AL operator to give an overall OP operator; and (iii) a map that describes how the parameters of these operators vary approximately periodically across V1. The parameters of the proposed model maximize the neural response at a given OP with an OP tuning curve fitted to experimental results. It is found that the anisotropy of the AL operator does not significantly affect OP selectivity, which is dominated by the RF anisotropy, consistent with Hubel and Wiesel's original conclusions that orientation tuning width of V1 simple cell is inversely related to the elongation of its RF. A simplified and idealized OP-OD map is then constructed to describe the approximately periodic local OP-OD structure of V1 in a compact form. It is shown explicitly that the OP map can be approximated by retaining its dominant spatial Fourier coefficients, which are shown to suffice to reconstruct its basic spatial structure. Moreover, this representation is a suitable form to analyze observed OP maps compactly and to be used in neural field theory (NFT) for analyzing activity modulated by the OP-OD structure of V1. Application to independently simulated V1 OP structure shows that observed irregularities in the map correspond to a spread of dominant coefficients in a circle in Fourier space. In addition, there is a strong bias toward two perpendicular directions when only a small patch of local map is included. The bias is decreased as the amount of V1 included in the Fourier transform is increased.
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Affiliation(s)
- Xiaochen Liu
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
- *Correspondence: Xiaochen Liu
| | - Peter A. Robinson
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
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16
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MIYASHITA Y. Operating principles of the cerebral cortex as a six-layered network in primates: beyond the classic canonical circuit model. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2022; 98:93-111. [PMID: 35283409 PMCID: PMC8948418 DOI: 10.2183/pjab.98.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/28/2021] [Indexed: 06/14/2023]
Abstract
The cerebral cortex performs its computations with many six-layered fundamental units, collectively spreading along the cortical sheet. What is the local network structure and the operating dynamics of such a fundamental unit? Previous investigations of primary sensory areas revealed a classic "canonical" circuit model, leading to an expectation of similar circuit organization and dynamics throughout the cortex. This review clarifies the different circuit dynamics at play in the higher association cortex of primates that implements computation for high-level cognition such as memory and attention. Instead of feedforward processing of response selectivity through Layers 4 to 2/3 that the classic canonical circuit stipulates, memory recall in primates occurs in Layer 5/6 with local backward projection to Layer 2/3, after which the retrieved information is sent back from Layer 6 to lower-level cortical areas for further retrieval of nested associations of target attributes. In this review, a novel "dynamic multimode module (D3M)" in the primate association cortex is proposed, as a new "canonical" circuit model performing this operation.
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Affiliation(s)
- Yasushi MIYASHITA
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
- Juntendo University, Graduate School of Medicine, Tokyo, Japan
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17
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Larisch R, Gönner L, Teichmann M, Hamker FH. Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Comput Biol 2021; 17:e1009566. [PMID: 34843455 PMCID: PMC8629393 DOI: 10.1371/journal.pcbi.1009566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/15/2021] [Indexed: 11/18/2022] Open
Abstract
Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network. Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.
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Affiliation(s)
- René Larisch
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- * E-mail: (RL); (FHH)
| | - Lorenz Gönner
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Faculty of Psychology, Lifespan Developmental Neuroscience, TU Dresden, Dresden, Germany
| | - Michael Teichmann
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
| | - Fred H. Hamker
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Bernstein Center Computational Neuroscience, Berlin, Germany
- * E-mail: (RL); (FHH)
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18
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Ahmadian Y, Miller KD. What is the dynamical regime of cerebral cortex? Neuron 2021; 109:3373-3391. [PMID: 34464597 PMCID: PMC9129095 DOI: 10.1016/j.neuron.2021.07.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 07/05/2021] [Accepted: 07/30/2021] [Indexed: 01/13/2023]
Abstract
Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.
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Affiliation(s)
- Yashar Ahmadian
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, and Department of Neuroscience, College of Physicians and Surgeons and Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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19
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Downer JD, Verhein JR, Rapone BC, O'Connor KN, Sutter ML. An Emergent Population Code in Primary Auditory Cortex Supports Selective Attention to Spectral and Temporal Sound Features. J Neurosci 2021; 41:7561-7577. [PMID: 34210783 PMCID: PMC8425978 DOI: 10.1523/jneurosci.0693-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/19/2021] [Accepted: 05/28/2021] [Indexed: 11/21/2022] Open
Abstract
Textbook descriptions of primary sensory cortex (PSC) revolve around single neurons' representation of low-dimensional sensory features, such as visual object orientation in primary visual cortex (V1), location of somatic touch in primary somatosensory cortex (S1), and sound frequency in primary auditory cortex (A1). Typically, studies of PSC measure neurons' responses along few (one or two) stimulus and/or behavioral dimensions. However, real-world stimuli usually vary along many feature dimensions and behavioral demands change constantly. In order to illuminate how A1 supports flexible perception in rich acoustic environments, we recorded from A1 neurons while rhesus macaques (one male, one female) performed a feature-selective attention task. We presented sounds that varied along spectral and temporal feature dimensions (carrier bandwidth and temporal envelope, respectively). Within a block, subjects attended to one feature of the sound in a selective change detection task. We found that single neurons tend to be high-dimensional, in that they exhibit substantial mixed selectivity for both sound features, as well as task context. We found no overall enhancement of single-neuron coding of the attended feature, as attention could either diminish or enhance this coding. However, a population-level analysis reveals that ensembles of neurons exhibit enhanced encoding of attended sound features, and this population code tracks subjects' performance. Importantly, surrogate neural populations with intact single-neuron tuning but shuffled higher-order correlations among neurons fail to yield attention- related effects observed in the intact data. These results suggest that an emergent population code not measurable at the single-neuron level might constitute the functional unit of sensory representation in PSC.SIGNIFICANCE STATEMENT The ability to adapt to a dynamic sensory environment promotes a range of important natural behaviors. We recorded from single neurons in monkey primary auditory cortex (A1), while subjects attended to either the spectral or temporal features of complex sounds. Surprisingly, we found no average increase in responsiveness to, or encoding of, the attended feature across single neurons. However, when we pooled the activity of the sampled neurons via targeted dimensionality reduction (TDR), we found enhanced population-level representation of the attended feature and suppression of the distractor feature. This dissociation of the effects of attention at the level of single neurons versus the population highlights the synergistic nature of cortical sound encoding and enriches our understanding of sensory cortical function.
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Affiliation(s)
- Joshua D Downer
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, California 94143
| | - Jessica R Verhein
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- School of Medicine, Stanford University, Stanford, California 94305
| | - Brittany C Rapone
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- School of Social Sciences, Oxford Brookes University, Oxford, OX4 0BP, United Kingdom
| | - Kevin N O'Connor
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, California 95618
| | - Mitchell L Sutter
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, California 95618
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20
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Festa D, Aschner A, Davila A, Kohn A, Coen-Cagli R. Neuronal variability reflects probabilistic inference tuned to natural image statistics. Nat Commun 2021; 12:3635. [PMID: 34131142 PMCID: PMC8206154 DOI: 10.1038/s41467-021-23838-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 05/19/2021] [Indexed: 11/23/2022] Open
Abstract
Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses-their variability-can be explained by a probabilistic representation tuned to naturalistic inputs.
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Affiliation(s)
- Dylan Festa
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Amir Aschner
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Aida Davila
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.
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21
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Wolff A, Chen L, Tumati S, Golesorkhi M, Gomez-Pilar J, Hu J, Jiang S, Mao Y, Longtin A, Northoff G. Prestimulus dynamics blend with the stimulus in neural variability quenching. Neuroimage 2021; 238:118160. [PMID: 34058331 DOI: 10.1016/j.neuroimage.2021.118160] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/30/2021] [Accepted: 05/09/2021] [Indexed: 01/08/2023] Open
Abstract
Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.
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Affiliation(s)
- Annemarie Wolff
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada.
| | - Liang Chen
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China.
| | - Shankar Tumati
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada
| | - Mehrshad Golesorkhi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red-Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain
| | - Jie Hu
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - Shize Jiang
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - Ying Mao
- Department of Neurological Surgery, Huashan Hospital, Fudan University, Wulumuqi Middle Rd, Shanghai, China
| | - André Longtin
- Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada; Physics Department, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- University of Ottawa Institute of Mental Health Research, Ottawa, Canada; Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada
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22
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Antolik J, Sabatier Q, Galle C, Frégnac Y, Benosman R. Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1. Sci Rep 2021; 11:10783. [PMID: 34031442 PMCID: PMC8144184 DOI: 10.1038/s41598-021-88960-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/01/2021] [Indexed: 02/04/2023] Open
Abstract
The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 [Formula: see text]m, and light intensity as weak as [Formula: see text] photons/s/cm[Formula: see text] is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.
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Affiliation(s)
- Jan Antolik
- Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00, Prague 1, Czechia.
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France.
| | - Quentin Sabatier
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
| | - Charlie Galle
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
| | - Yves Frégnac
- Unité de Neurosciences, Information et Complexité (UNIC), NeuroPSI, Gif-sur-Yvette, France
| | - Ryad Benosman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012, Paris, France
- University of Pittsburgh, McGowan Institute, 3025 E Carson St, Pittsburgh, PA, USA
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23
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Georgeson MA, Sengpiel F. Contrast adaptation and interocular transfer in cortical cells: A re-analysis & a two-stage gain-control model of binocular combination. Vision Res 2021; 185:29-49. [PMID: 33894463 DOI: 10.1016/j.visres.2021.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 01/17/2021] [Accepted: 03/09/2021] [Indexed: 11/28/2022]
Abstract
How do V1 cells respond to, adapt to, and combine signals from the two eyes? We tested a simple functional model that has monocular and binocular stages of divisive contrast gain control (CGC) that sit before, and after, binocular summation respectively. Interocular suppression (IOS) was another potential influence on contrast gain. Howarth, Vorobyov & Sengpiel (2009, Cerebral Cortex, 19, 1835-1843) studied contrast adaptation and interocular transfer in cat V1 cells. In our re-analysis we found that ocular dominance (OD) and contrast adaptation at a fixed test contrast were well described by a re-scaling of the unadapted orientation tuning curve - a simple change in response gain. We compared six variants of the basic model, and one model fitted the gain data notably better than the others did. When the dominant eye was tested, adaptation reduced cell response gain more when that eye was adapted than when the other eye was adapted. But when the non-dominant eye was tested, adapting either eye gave about the same reduction in overall gain, and there was an interaction between OD and adapting eye that was well described by the best-fitting model. Two key features of this model are that signals driving IOS arise 'early', before attenuation due to OD, while suppressive CGC signals are 'late' and so affected by OD. We show that late CGC confers a functional advantage: it yields partial compensation for OD, which should reduce ocular imbalance at the input to binocular summation, and improve the cell's sensitivity to variation in stereo disparity.
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Affiliation(s)
- Mark A Georgeson
- College of Health & Life Sciences, Aston University, B4 7ET, UK.
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24
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Chavane F, Contreras D. Like a clock in the rabbit's visual cortex. eLife 2021; 10:65581. [PMID: 33538694 PMCID: PMC7861611 DOI: 10.7554/elife.65581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 01/28/2021] [Indexed: 11/23/2022] Open
Abstract
Three rules govern the connectivity between neurons in the thalamus and inhibitory neurons in the visual cortex of rabbits.
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Affiliation(s)
- Frédéric Chavane
- Institut de Neurosciences de la Timone, UMR 7289, CNRS and the Faculté de Médecine, Aix-Marseille Université, Marseille, France.,Faculté de Médecine, Aix-Marseille Université, Marseille, France
| | - Diego Contreras
- Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, United States
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25
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Mukherjee A, Bajwa N, Lam NH, Porrero C, Clasca F, Halassa MM. Variation of connectivity across exemplar sensory and associative thalamocortical loops in the mouse. eLife 2020; 9:e62554. [PMID: 33103997 PMCID: PMC7644223 DOI: 10.7554/elife.62554] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 10/23/2020] [Indexed: 12/22/2022] Open
Abstract
The thalamus engages in sensation, action, and cognition, but the structure underlying these functions is poorly understood. Thalamic innervation of associative cortex targets several interneuron types, modulating dynamics and influencing plasticity. Is this structure-function relationship distinct from that of sensory thalamocortical systems? Here, we systematically compared function and structure across a sensory and an associative thalamocortical loop in the mouse. Enhancing excitability of mediodorsal thalamus, an associative structure, resulted in prefrontal activity dominated by inhibition. Equivalent enhancement of medial geniculate excitability robustly drove auditory cortical excitation. Structurally, geniculate axons innervated excitatory cortical targets in a preferential manner and with larger synaptic terminals, providing a putative explanation for functional divergence. The two thalamic circuits also had distinct input patterns, with mediodorsal thalamus receiving innervation from a diverse set of cortical areas. Altogether, our findings contribute to the emerging view of functional diversity across thalamic microcircuits and its structural basis.
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Affiliation(s)
- Arghya Mukherjee
- McGovern Institute for Brain ResearchCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Navdeep Bajwa
- McGovern Institute for Brain ResearchCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Norman H Lam
- McGovern Institute for Brain ResearchCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
| | - César Porrero
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid UniversityMadridSpain
| | - Francisco Clasca
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid UniversityMadridSpain
| | - Michael M Halassa
- McGovern Institute for Brain ResearchCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
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26
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Sanzeni A, Histed MH, Brunel N. Response nonlinearities in networks of spiking neurons. PLoS Comput Biol 2020; 16:e1008165. [PMID: 32941457 PMCID: PMC7524009 DOI: 10.1371/journal.pcbi.1008165] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 09/29/2020] [Accepted: 07/19/2020] [Indexed: 01/18/2023] Open
Abstract
Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.
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Affiliation(s)
- Alessandro Sanzeni
- National institute of Mental Health Intramural Program, NIH, Bethesda, MD, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Mark H. Histed
- National institute of Mental Health Intramural Program, NIH, Bethesda, MD, USA
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, NC, USA
- Department of Physics, Duke University, Durham, NC, USA
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27
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Cortese A, Lau H, Kawato M. Unconscious reinforcement learning of hidden brain states supported by confidence. Nat Commun 2020; 11:4429. [PMID: 32868772 PMCID: PMC7459278 DOI: 10.1038/s41467-020-17828-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 07/13/2020] [Indexed: 12/11/2022] Open
Abstract
Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity. Reward contingencies are defined in real-time by fMRI multivoxel patterns; optimal action policies thereby depend on multidimensional brain activity taking place below the threshold of consciousness, by design. We find that subjects can solve the task within two hundred trials and errors, as their reinforcement learning processes interact with metacognitive functions (quantified as the meaningfulness of their decision confidence). Computational modelling and multivariate analyses identify a frontostriatal neural mechanism by which the brain may untangle the 'curse of dimensionality': synchronization of confidence representations in prefrontal cortex with reward prediction errors in basal ganglia support exploration of latent task representations. These results may provide an alternative starting point for future investigations into unconscious learning and functions of metacognition.
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Affiliation(s)
- Aurelio Cortese
- Computational Neuroscience Laboratories, ATR Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
| | - Hakwan Lau
- Department of Psychology, UCLA, 1285 Franz Hall, Los Angeles, CA, 90095, USA
- Brain Research Institute, UCLA, 695 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Psychology, University of Hong Kong, 627, The Jockey Club Tower, Pok Fu Lam Rd, Pok Fu Lam, Hong Kong
- State Key Laboratory for Brain and Cognitive Sciences, University of Hong Kong, 5 Sassoon Rd, Pok Fu Lam, Hong Kong
| | - Mitsuo Kawato
- Computational Neuroscience Laboratories, ATR Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
- RIKEN Center for Advanced Intelligence Project, ATR Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-Gun, Kyoto, 619-0288, Japan.
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28
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Roy A, Osik JJ, Meschede-Krasa B, Alford WT, Leman DP, Van Hooser SD. Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity. eLife 2020; 9:e58509. [PMID: 32701059 PMCID: PMC7440916 DOI: 10.7554/elife.58509] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/23/2020] [Indexed: 02/02/2023] Open
Abstract
Modifications of synaptic inputs and cell-intrinsic properties both contribute to neuronal plasticity and development. To better understand these mechanisms, we undertook an intracellular analysis of the development of direction selectivity in the ferret visual cortex, which occurs rapidly over a few days after eye opening. We found strong evidence of developmental changes in linear spatiotemporal receptive fields of simple cells, implying alterations in circuit inputs. Further, this receptive field plasticity was accompanied by increases in near-spike-threshold excitability and input-output gain that resulted in dramatically increased spiking responses in the experienced state. Increases in subthreshold membrane responses induced by the receptive field plasticity and the increased input-output spiking gain were both necessary to explain the elevated firing rates in experienced ferrets. These results demonstrate that cortical direction selectivity develops through a combination of plasticity in inputs and in cell-intrinsic properties.
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Affiliation(s)
- Arani Roy
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | - Jason J Osik
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | | | - Wesley T Alford
- Department of Biology, Brandeis UniversityWalthamUnited States
| | - Daniel P Leman
- Department of Biology, Brandeis UniversityWalthamUnited States
| | - Stephen D Van Hooser
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
- Sloan-Swartz Center for Theoretical Neurobiology Brandeis UniversityWalthamUnited States
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29
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Fruend I. Constrained sampling from deep generative image models reveals mechanisms of human target detection. J Vis 2020; 20:32. [PMID: 32729908 PMCID: PMC7424951 DOI: 10.1167/jov.20.7.32] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The first steps of visual processing are often described as a bank of oriented filters followed by divisive normalization. This approach has been tremendously successful at predicting contrast thresholds in simple visual displays. However, it is unclear to what extent this kind of architecture also supports processing in more complex visual tasks performed in naturally looking images. We used a deep generative image model to embed arc segments with different curvatures in naturalistic images. These images contain the target as part of the image scene, resulting in considerable appearance variation of target as well as background. Three observers localized arc targets in these images, with an average accuracy of 74.7%. Data were fit by several biologically inspired models, four standard deep convolutional neural networks (CNNs), and a five-layer CNN specifically trained for this task. Four models predicted observer responses particularly well; (1) a bank of oriented filters, similar to complex cells in primate area V1; (2) a bank of oriented filters followed by tuned gain control, incorporating knowledge about cortical surround interactions; (3) a bank of oriented filters followed by local normalization; and (4) the five-layer CNN. A control experiment with optimized stimuli based on these four models showed that the observers' data were best explained by model (2) with tuned gain control. These data suggest that standard models of early vision provide good descriptions of performance in much more complex tasks than what they were designed for, while general-purpose non linear models such as convolutional neural networks do not.
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Affiliation(s)
- Ingo Fruend
- Department of Psychology, Centre for Vision Research & Vision: Science to Application, York University, Toronto, ON, Canada
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30
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Cooke JE, Kahn MC, Mann EO, King AJ, Schnupp JWH, Willmore BDB. Contrast gain control occurs independently of both parvalbumin-positive interneuron activity and shunting inhibition in auditory cortex. J Neurophysiol 2020; 123:1536-1551. [PMID: 32186432 PMCID: PMC7191518 DOI: 10.1152/jn.00587.2019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 03/16/2020] [Accepted: 03/18/2020] [Indexed: 12/31/2022] Open
Abstract
Contrast gain control is the systematic adjustment of neuronal gain in response to the contrast of sensory input. It is widely observed in sensory cortical areas and has been proposed to be a canonical neuronal computation. Here, we investigated whether shunting inhibition from parvalbumin-positive interneurons-a mechanism involved in gain control in visual cortex-also underlies contrast gain control in auditory cortex. First, we performed extracellular recordings in the auditory cortex of anesthetized male mice and optogenetically manipulated the activity of parvalbumin-positive interneurons while varying the contrast of the sensory input. We found that both activation and suppression of parvalbumin interneuron activity altered the overall gain of cortical neurons. However, despite these changes in overall gain, we found that manipulating parvalbumin interneuron activity did not alter the strength of contrast gain control in auditory cortex. Furthermore, parvalbumin-positive interneurons did not show increases in activity in response to high-contrast stimulation, which would be expected if they drive contrast gain control. Finally, we performed in vivo whole-cell recordings in auditory cortical neurons during high- and low-contrast stimulation and found that no increase in membrane conductance was observed during high-contrast stimulation. Taken together, these findings indicate that while parvalbumin-positive interneuron activity modulates the overall gain of auditory cortical responses, other mechanisms are primarily responsible for contrast gain control in this cortical area.NEW & NOTEWORTHY We investigated whether contrast gain control is mediated by shunting inhibition from parvalbumin-positive interneurons in auditory cortex. We performed extracellular and intracellular recordings in mouse auditory cortex while presenting sensory stimuli with varying contrasts and manipulated parvalbumin-positive interneuron activity using optogenetics. We show that while parvalbumin-positive interneuron activity modulates the gain of cortical responses, this activity is not the primary mechanism for contrast gain control in auditory cortex.
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Affiliation(s)
- James E Cooke
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
- University College London, London, United Kingdom
| | - Martin C Kahn
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Edward O Mann
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Andrew J King
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Jan W H Schnupp
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong
| | - Ben D B Willmore
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
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31
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Halassa MM, Sherman SM. Thalamocortical Circuit Motifs: A General Framework. Neuron 2020; 103:762-770. [PMID: 31487527 DOI: 10.1016/j.neuron.2019.06.005] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/28/2019] [Accepted: 06/11/2019] [Indexed: 12/13/2022]
Abstract
The role of the thalamus in cortical sensory transmission is well known, but its broader role in cognition is less appreciated. Recent studies have shown thalamic engagement in dynamic regulation of cortical activity in attention, executive control, and perceptual decision-making, but the circuit mechanisms underlying such functionality are unknown. Because the thalamus is composed of excitatory neurons that are devoid of local recurrent excitatory connectivity, delineating long-range, input-output connectivity patterns of single thalamic neurons is critical for building functional models. We discuss this need in relation to existing organizational schemes such as core versus matrix and first-order versus higher-order relay nuclei. We propose that a new classification is needed based on thalamocortical motifs, where structure naturally informs function. Overall, our synthesis puts understanding thalamic organization at the forefront of existing research in systems and computational neuroscience, with both basic and translational applications.
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Affiliation(s)
- Michael M Halassa
- Department of Brain and Cognitive Science and the McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - S Murray Sherman
- Department of Neurobiology, University of Chicago School of Medicine, Chicago, IL, USA.
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32
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Neurophysiological basis of contrast dependent BOLD orientation tuning. Neuroimage 2020; 206:116323. [PMID: 31678228 DOI: 10.1016/j.neuroimage.2019.116323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/03/2019] [Accepted: 10/29/2019] [Indexed: 11/22/2022] Open
Abstract
Recent work in early visual cortex of humans has shown that the BOLD signal exhibits contrast dependent orientation tuning, with an inverse oblique effect (oblique > cardinal) at high contrast and a horizontal effect (vertical > horizontal) at low contrast. This finding is at odds with decades of neurophysiological research demonstrating contrast invariant orientation tuning in primate visual cortex, yet the source of this discrepancy is unclear. We hypothesized that contrast dependent BOLD orientation tuning may arise due to contrast dependent influences of feedforward (FF) and feedback (FB) synaptic activity, indexed through gamma and alpha rhythms, respectively. To quantify this, we acquired EEG and BOLD in healthy humans to generate and compare orientation tuning curves across all neural frequency bands with BOLD. As expected, BOLD orientation selectivity in V1 was contrast dependent, preferring oblique orientations at high contrast and vertical at low contrast. On the other hand, EEG orientation tuning was contrast invariant, though frequency-specific, with an inverse-oblique effect in the gamma band (FF) and a horizontal effect in the alpha band (FB). Therefore, high-contrast BOLD orientation tuning closely matched FF activity, while at low contrast, BOLD best resembled FB orientation tuning. These results suggest that contrast dependent BOLD orientation tuning arises due to the reduced contribution of FF input to overall neurophysiological activity at low contrast, shifting BOLD orientation tuning towards the orientation preferences of FB at low contrast.
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33
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Mechanisms underlying gain modulation in the cortex. Nat Rev Neurosci 2020; 21:80-92. [PMID: 31911627 DOI: 10.1038/s41583-019-0253-y] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2019] [Indexed: 01/19/2023]
Abstract
Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.
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34
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Lieber JD, Bensmaia SJ. Emergence of an Invariant Representation of Texture in Primate Somatosensory Cortex. Cereb Cortex 2019; 30:3228-3239. [PMID: 31813989 PMCID: PMC7197205 DOI: 10.1093/cercor/bhz305] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 11/08/2019] [Accepted: 11/12/2019] [Indexed: 01/13/2023] Open
Abstract
A major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons (from Brodmann areas 3b, 1, and 2). We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.
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Affiliation(s)
- Justin D Lieber
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Sliman J Bensmaia
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, 60637, USA.,Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, 60637, USA
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35
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Gáspár ME, Polack PO, Golshani P, Lengyel M, Orbán G. Representational untangling by the firing rate nonlinearity in V1 simple cells. eLife 2019; 8:43625. [PMID: 31502537 PMCID: PMC6739864 DOI: 10.7554/elife.43625] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/13/2019] [Indexed: 11/13/2022] Open
Abstract
An important computational goal of the visual system is ‘representational untangling’ (RU): representing increasingly complex features of visual scenes in an easily decodable format. RU is typically assumed to be achieved in high-level visual cortices via several stages of cortical processing. Here we show, using a canonical population coding model, that RU of low-level orientation information is already performed at the first cortical stage of visual processing, but not before that, by a fundamental cellular-level property: the thresholded firing rate nonlinearity of simple cells in the primary visual cortex (V1). We identified specific, experimentally measurable parameters that determined the optimal firing threshold for RU and found that the thresholds of V1 simple cells extracted from in vivo recordings in awake behaving mice were near optimal. These results suggest that information re-formatting, rather than maximisation, may already be a relevant computational goal for the early visual system.
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Affiliation(s)
- Merse E Gáspár
- MTA Wigner Research Center for Physics, Budapest, Hungary.,Department of Cognitive Science, Central European University, Budapest, Hungary
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, United States
| | - Peyman Golshani
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, United States.,Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.,West Los Angeles VA Medical Center, Los Angeles, United States
| | - Máté Lengyel
- Department of Cognitive Science, Central European University, Budapest, Hungary.,Department of Engineering, Computational and Biological Learning Lab, University of Cambridge, Cambridge, United Kingdom
| | - Gergő Orbán
- MTA Wigner Research Center for Physics, Budapest, Hungary
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36
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Hennequin G, Ahmadian Y, Rubin DB, Lengyel M, Miller KD. The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability. Neuron 2019; 98:846-860.e5. [PMID: 29772203 PMCID: PMC5971207 DOI: 10.1016/j.neuron.2018.04.017] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 02/14/2018] [Accepted: 04/12/2018] [Indexed: 12/16/2022]
Abstract
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception. A simple network model explains stimulus-tuning of cortical variability suppression Inhibition stabilizes recurrently interacting neurons with supralinear I/O functions Stimuli strengthen inhibitory stabilization around a stable state, quenching variability Single-trial V1 data are compatible with this model and rules out competing proposals
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Affiliation(s)
- Guillaume Hennequin
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
| | - Yashar Ahmadian
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Centre de Neurophysique, Physiologie, et Pathologie, CNRS, 75270 Paris Cedex 06, France; Institute of Neuroscience, Department of Biology and Mathematics, University of Oregon, Eugene, OR 97403, USA
| | - Daniel B Rubin
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neurology, Massachusetts General Hospital and Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; Department of Cognitive Science, Central European University, 1051 Budapest, Hungary
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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37
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Neural Variability Is Quenched by Attention. J Neurosci 2019; 39:5975-5985. [PMID: 31152124 DOI: 10.1523/jneurosci.0355-19.2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/08/2019] [Accepted: 04/21/2019] [Indexed: 01/09/2023] Open
Abstract
Attention can be subdivided into several components, including alertness and spatial attention. It is believed that the behavioral benefits of attention, such as increased accuracy and faster reaction times, are generated by an increase in neural activity and a decrease in neural variability, which enhance the signal-to-noise ratio of task-relevant neural populations. However, empirical evidence regarding attention-related changes in neural variability in humans is extremely rare. Here we used EEG to demonstrate that trial-by-trial neural variability was reduced by visual cues that modulated alertness and spatial attention. Reductions in neural variability were specific to the visual system and larger in the contralateral hemisphere of the attended visual field. Subjects with higher initial levels of neural variability and larger decreases in variability exhibited greater behavioral benefits from attentional cues. These findings demonstrate that both alertness and spatial attention modulate neural variability and highlight the importance of reducing/quenching neural variability for attaining the behavioral benefits of attention.SIGNIFICANCE STATEMENT Attention is thought to improve perception by increasing the signal-to-noise ratio of the neuronal populations that encode the attended stimulus. Signal-to-noise ratio can be enhanced by increasing neural response (signal) and/or by reducing neural variability (noise). The ability of attention to increase neural responses has been studied extensively, but the effects of attention on neural variability have rarely been examined in humans. Here, we demonstrate that modulating different components of attention, including alertness and spatial attention, reduces neural variability in humans. Furthermore, we show that subjects with larger reductions in neural variability exhibit greater behavioral benefits from attention. These results demonstrate that reduction of neural variability is a fundamental feature of attentional processes in humans with clear behavioral importance.
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38
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Neural variability quenching during decision-making: Neural individuality and its prestimulus complexity. Neuroimage 2019; 192:1-14. [DOI: 10.1016/j.neuroimage.2019.02.070] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 01/31/2019] [Accepted: 02/27/2019] [Indexed: 11/20/2022] Open
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39
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Stochastic neural field model of stimulus-dependent variability in cortical neurons. PLoS Comput Biol 2019; 15:e1006755. [PMID: 30883546 PMCID: PMC6438587 DOI: 10.1371/journal.pcbi.1006755] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/28/2019] [Accepted: 02/26/2019] [Indexed: 01/03/2023] Open
Abstract
We use stochastic neural field theory to analyze the stimulus-dependent tuning of neural variability in ring attractor networks. We apply perturbation methods to show how the neural field equations can be reduced to a pair of stochastic nonlinear phase equations describing the stochastic wandering of spontaneously formed tuning curves or bump solutions. These equations are analyzed using a modified version of the bivariate von Mises distribution, which is well-known in the theory of circular statistics. We first consider a single ring network and derive a simple mathematical expression that accounts for the experimentally observed bimodal (or M-shaped) tuning of neural variability. We then explore the effects of inter-network coupling on stimulus-dependent variability in a pair of ring networks. These could represent populations of cells in two different layers of a cortical hypercolumn linked via vertical synaptic connections, or two different cortical hypercolumns linked by horizontal patchy connections within the same layer. We find that neural variability can be suppressed or facilitated, depending on whether the inter-network coupling is excitatory or inhibitory, and on the relative strengths and biases of the external stimuli to the two networks. These results are consistent with the general observation that increasing the mean firing rate via external stimuli or modulating drives tends to reduce neural variability.
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40
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Lian Y, Grayden DB, Kameneva T, Meffin H, Burkitt AN. Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding. Front Neural Circuits 2019; 13:13. [PMID: 30930752 PMCID: PMC6427952 DOI: 10.3389/fncir.2019.00013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/19/2019] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.
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Affiliation(s)
- Yanbo Lian
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Tatiana Kameneva
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Faculty of Science, Engineering and Technology, Swinburne University, Melbourne, VIC, Australia
| | - Hamish Meffin
- Department of Optometry and Visual Science, The University of Melbourne, Melbourne, VIC, Australia.,National Vision Research Institute, The Australian College of Optometry, Melbourne, VIC, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
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41
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Gardner JL, Liu T. Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus. eNeuro 2019; 6:ENEURO.0363-18.2019. [PMID: 30923743 PMCID: PMC6437661 DOI: 10.1523/eneuro.0363-18.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 01/24/2023] Open
Abstract
Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay "population-level stimulus representations" (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.
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Affiliation(s)
| | - Taosheng Liu
- Department of Psychology, Michigan State University, East Lansing, MI 48824
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42
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Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation. Nat Commun 2019; 10:941. [PMID: 30808863 PMCID: PMC6391449 DOI: 10.1038/s41467-019-08894-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 02/05/2019] [Indexed: 11/08/2022] Open
Abstract
Sensory systems face a barrage of stimulation that continually changes along multiple dimensions. These simultaneous changes create a formidable problem for the nervous system, as neurons must dynamically encode each stimulus dimension, despite changes in other dimensions. Here, we measured how neurons in visual cortex encode orientation following changes in luminance and contrast, which are critical for visual processing, but nuisance variables in the context of orientation coding. Using information theoretic analysis and population decoding approaches, we find that orientation discriminability is luminance and contrast dependent, changing over time due to firing rate adaptation. We also show that orientation discrimination in human observers changes during adaptation, in a manner consistent with the neuronal data. Our results suggest that adaptation does not maintain information rates per se, but instead acts to keep sensory systems operating within the limited dynamic range afforded by spiking activity, despite a wide range of possible inputs. Sensory systems produce stable stimulus representations despite constant changes across multiple stimulus dimensions. Here, the authors reveal dynamic neural coding mechanisms by testing how coding of one dimension (orientation) changes with adaptations to other dimensions (luminance and contrast).
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43
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Internal Gain Modulations, But Not Changes in Stimulus Contrast, Preserve the Neural Code. J Neurosci 2019; 39:1671-1687. [PMID: 30647148 DOI: 10.1523/jneurosci.2012-18.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 12/14/2018] [Accepted: 01/06/2019] [Indexed: 11/21/2022] Open
Abstract
Neurons in primary visual cortex are strongly modulated both by stimulus contrast and by fluctuations of internal inputs. An important question is whether the population code is preserved under these conditions. Changes in stimulus contrast are thought to leave the population code invariant, whereas the effect of internal gain modulations remains unknown. To address these questions we studied how the direction-of-motion of oriented gratings is encoded in layer 2/3 primary visual cortex of mouse (with C57BL/6 background, of either sex). We found that, because contrast gain responses across cells are heterogeneous, a change in contrast alters the information distribution profile across cells leading to a violation of contrast invariance. Remarkably, internal input fluctuations that cause commensurate firing rate modulations at the single-cell level result in more homogeneous gain responses, respecting population code invariance. These observations argue that the brain strives to maintain the stability of the neural code in the face of fluctuating internal inputs.SIGNIFICANCE STATEMENT Neuronal responses are modulated both by stimulus contrast and by the spontaneous fluctuation of internal inputs. It is not well understood how these different types of input impact the population code. Specifically, it is important to understand whether the neural code stays invariant in the face of significant internal input modulations. Here, we show that changes in stimulus contrast lead to different optimal population codes, whereas spontaneous internal input fluctuations leave the population code invariant. This is because spontaneous internal input fluctuations modulate the gain of neuronal responses more homogeneously across cells compared to changes in stimulus contrast.
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44
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Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Abbasi-Asl R, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-Llavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C. Visual physiology of the layer 4 cortical circuit in silico. PLoS Comput Biol 2018; 14:e1006535. [PMID: 30419013 PMCID: PMC6258373 DOI: 10.1371/journal.pcbi.1006535] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 11/26/2018] [Accepted: 09/29/2018] [Indexed: 01/15/2023] Open
Abstract
Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations. How can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.
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Affiliation(s)
- Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nathan W Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yazan N Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ramakrishnan Iyer
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Zihao Xu
- University of California San Diego, La Jolla, CA, United States of America
| | - Reza Abbasi-Asl
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Michael Buice
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nuno da Costa
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Saskia de Vries
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Denman
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Severine Durand
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jérôme Lecoq
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Lu Li
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Gabriel K Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | | | - Staci A Sorensen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Massimo Scanziani
- Howard Hughes Medical Institute and Department of Physiology, University of California San Francisco, San Francisco, California, United States of America
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, United States of America
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45
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Abstract
Recently, sophisticated optogenetic tools for mouse have enabled many detailed studies of the neuronal circuits of its primary visual cortex (V1), providing much more specific information than is available for cat or monkey. Among various other differences, they show a striking contrast dependency in orientation selectivity in mouse V1 rather than the well-known contrast invariance for cat and monkey. Constrained by the existing experiment data, we develop a comprehensive large-scale model of an effective input layer of mouse V1 that successfully reproduces the contrast-dependent phenomena and many other response properties. The model helps to probe different mechanisms based on excitation–inhibition balance that underlie both contrast dependencies and invariance, and it provides implications for future studies on these circuits. Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).
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46
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Lynch LK, Lu KH, Wen H, Zhang Y, Saykin AJ, Liu Z. Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions. Hum Brain Mapp 2018; 39:4939-4948. [PMID: 30144210 DOI: 10.1002/hbm.24335] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 07/12/2018] [Accepted: 07/16/2018] [Indexed: 11/11/2022] Open
Abstract
During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.
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Affiliation(s)
- Lauren K Lynch
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Kun-Han Lu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Haiguang Wen
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Yizhen Zhang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
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47
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Shi X, Jin Y, Cang J. Transformation of Feature Selectivity From Membrane Potential to Spikes in the Mouse Superior Colliculus. Front Cell Neurosci 2018; 12:163. [PMID: 29970991 PMCID: PMC6018398 DOI: 10.3389/fncel.2018.00163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 05/25/2018] [Indexed: 11/13/2022] Open
Abstract
Neurons in the visual system display varying degrees of selectivity for stimulus features such as orientation and direction. Such feature selectivity is generated and processed by intricate circuit and synaptic mechanisms. A key factor in this process is the input-output transformation from membrane potential (Vm) to spikes in individual neurons. Here, we use in vivo whole-cell recording to study Vm-to-spike transformation of visual feature selectivity in the superficial neurons of the mouse superior colliculus (SC). As expected from the spike threshold effect, direction and orientation selectivity increase from Vm to spike responses. The degree of this increase is highly variable, and interestingly, it is correlated with the receptive field size of the recorded neurons. We find that the relationships between Vm and spike rate and between Vm dynamics and spike initiation are also correlated with receptive field size, which likely contribute to the observed input-output transformation of feature selectivity. Together, our findings provide useful information for understanding information processing and visual transformation in the mouse SC.
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Affiliation(s)
- Xuefeng Shi
- Department of Neurobiology, Northwestern University, Evanston, IL, United States.,Tianjin Eye Hospital, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin Eye Institute, Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Yanjiao Jin
- Department of Neurobiology, Northwestern University, Evanston, IL, United States.,General Hospital, Tianjin Medical University, Tianjin, China
| | - Jianhua Cang
- Department of Neurobiology, Northwestern University, Evanston, IL, United States.,Department of Biology and Department of Psychology, University of Virginia, Charlottesville, VA, United States
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48
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Huang Z, Zhang J, Wu J, Liu X, Xu J, Zhang J, Qin P, Dai R, Yang Z, Mao Y, Hudetz AG, Northoff G. Disrupted neural variability during propofol-induced sedation and unconsciousness. Hum Brain Mapp 2018; 39:4533-4544. [PMID: 29974570 DOI: 10.1002/hbm.24304] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 06/04/2018] [Accepted: 06/24/2018] [Indexed: 12/16/2022] Open
Abstract
Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, Michigan
| | - Jun Zhang
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jinsong Wu
- Neurological Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Xiaoge Liu
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jianghui Xu
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jianfeng Zhang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Pengmin Qin
- School of Psychology, South China Normal University, Guangzhou, People's Republic of China
| | - Rui Dai
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zhong Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Ying Mao
- Neurological Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Anthony G Hudetz
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, Michigan
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada.,Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, People's Republic of China.,Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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49
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Huang Z, Zhang J, Longtin A, Dumont G, Duncan NW, Pokorny J, Qin P, Dai R, Ferri F, Weng X, Northoff G. Is There a Nonadditive Interaction Between Spontaneous and Evoked Activity? Phase-Dependence and Its Relation to the Temporal Structure of Scale-Free Brain Activity. Cereb Cortex 2018; 27:1037-1059. [PMID: 26643354 DOI: 10.1093/cercor/bhv288] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The aim of our study was to use functional magnetic resonance imaging to investigate how spontaneous activity interacts with evoked activity, as well as how the temporal structure of spontaneous activity, that is, long-range temporal correlations, relate to this interaction. Using an extremely sparse event-related design (intertrial intervals: 52-60 s), a novel blood oxygen level-dependent signal correction approach (accounting for spontaneous fluctuations using pseudotrials) and phase analysis, we provided direct evidence for a nonadditive interaction between spontaneous and evoked activity. We demonstrated the discrepancy between the present and previous observations on why a linear superposition between spontaneous and evoked activity can be seen by using co-occurring signals from homologous brain regions. Importantly, we further demonstrated that the nonadditive interaction can be characterized by phase-dependent effects of spontaneous activity, which is closely related to the degree of long-range temporal correlations in spontaneous activity as indexed by both power-law exponent and phase-amplitude coupling. Our findings not only contribute to the understanding of spontaneous brain activity and its scale-free properties, but also bear important implications for our understanding of neural activity in general.
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Affiliation(s)
- Zirui Huang
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
| | - Jianfeng Zhang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, PR China
| | - André Longtin
- Department of Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Grégory Dumont
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada.,Department of Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Niall W Duncan
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada.,Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China.,Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 23561, Taiwan
| | - Johanna Pokorny
- Department of Anthropology, University of Toronto, Toronto, ON M5S 2S2, Canada
| | - Pengmin Qin
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada.,Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 23561, Taiwan
| | - Rui Dai
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, PR China.,School of Life Science, South China Normal University, Guangzhou 510613, PR China
| | - Francesca Ferri
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
| | - Xuchu Weng
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, PR China
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada.,Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, PR China.,Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 23561, Taiwan
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50
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Abstract
The mechanisms underlying the emergence of orientation selectivity in the visual cortex have been, and continue to be, the subjects of intense scrutiny. Orientation selectivity reflects a dramatic change in the representation of the visual world: Whereas afferent thalamic neurons are generally orientation insensitive, neurons in the primary visual cortex (V1) are extremely sensitive to stimulus orientation. This profound change in the receptive field structure along the visual pathway has positioned V1 as a model system for studying the circuitry that underlies neural computations across the neocortex. The neocortex is characterized anatomically by the relative uniformity of its circuitry despite its role in processing distinct signals from region to region. A combination of physiological, anatomical, and theoretical studies has shed some light on the circuitry components necessary for generating orientation selectivity in V1. This targeted effort has led to critical insights, as well as controversies, concerning how neural circuits in the neocortex perform computations.
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Affiliation(s)
- Nicholas J Priebe
- Center for Learning and Memory, Center for Perceptual Systems, Department of Neuroscience, College of Natural Sciences, University of Texas, Austin, Texas 78712;
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