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Ramaswamy S. Data-driven multiscale computational models of cortical and subcortical regions. Curr Opin Neurobiol 2024; 85:102842. [PMID: 38320453 DOI: 10.1016/j.conb.2024.102842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.
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Affiliation(s)
- Srikanth Ramaswamy
- Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle Upon Tyne, NE2 4HH, United Kingdom.
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2
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Schulte to Brinke T, Duarte R, Morrison A. Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model. Front Integr Neurosci 2022; 16:923468. [PMID: 36310713 PMCID: PMC9615567 DOI: 10.3389/fnint.2022.923468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022] Open
Abstract
The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.
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Affiliation(s)
- Tobias Schulte to Brinke
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
- *Correspondence: Tobias Schulte to Brinke
| | - Renato Duarte
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
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Vanni S, Hokkanen H, Werner F, Angelucci A. Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models. Cereb Cortex 2020; 30:3483-3517. [PMID: 31897474 PMCID: PMC7233004 DOI: 10.1093/cercor/bhz322] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/02/2019] [Indexed: 12/22/2022] Open
Abstract
The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.
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Affiliation(s)
- Simo Vanni
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
| | - Henri Hokkanen
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
| | - Francesca Werner
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy
| | - Alessandra Angelucci
- Department of Ophthalmology and Visual Sciences, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
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Wang X, Zhang B, Wang H, Liu J, Xu G, Zhou Y. Aging affects correlation within the V1 neuronal population in rhesus monkeys. Neurobiol Aging 2019; 76:1-8. [PMID: 30599290 DOI: 10.1016/j.neurobiolaging.2018.11.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 11/29/2018] [Accepted: 11/29/2018] [Indexed: 10/27/2022]
Abstract
Visual function declines with age. This deterioration results not only from changes in the optical system but also from the functional degradation of the central visual cortex. Although numerous studies have explored the mechanisms of age-related influences on vision, they have failed to acknowledge the significance of neuronal correlation in dysfunction of the visual cortex. Previous research has focused on the functional degradation of individual neurons, with age-induced changes in correlation between neurons still unknown. In the present study, using electrophysiological techniques, we investigated the age-related changes in neuronal correlation in the macaque V1 area and the underlying mechanisms of those changes. Our results showed that aging led to an increase in the correlation of neurons and changed the noise-signal correlation structure, which may impact population coding efficiency. Furthermore, we found that the age-induced decline in the inhibitory circuitry accounted for the alteration in neuronal correlation.
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Affiliation(s)
- Xuan Wang
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China
| | - Bing Zhang
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China
| | - Huan Wang
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China
| | - Jiachen Liu
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China
| | - Guangwei Xu
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China.
| | - Yifeng Zhou
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei, Anhui, P.R.China; State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, P.R.China; Neurodegenerative Disorder Research Center and Brain Bank, Material Science at Microscale National Laboratory, School of Life Sciences, Key Laboratory of Brain Function and Disease, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui, P.R.China.
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Adjusted regularization of cortical covariance. J Comput Neurosci 2018; 45:83-101. [PMID: 30191352 DOI: 10.1007/s10827-018-0692-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 07/13/2018] [Accepted: 07/30/2018] [Indexed: 12/31/2022]
Abstract
It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an [Formula: see text] penalty. However, the partial correlations found in neural spike count data are neither large nor sparse, so techniques that perform well in sparse settings will typically perform poorly in the context of neural spike count data. Fortunately, the correlated firing for any pair of cortical neurons depends strongly on both their distance apart and the features for which they are tuned. We introduce a method that takes advantage of these known, strong effects by allowing the penalty to depend on them: thus, for example, the connection between pairs of neurons that are close together will be penalized less than pairs that are far apart. We show through simulations that this physiologically-motivated procedure performs substantially better than off-the-shelf generic tools, and we illustrate by applying the methodology to populations of neurons recorded with multielectrode arrays implanted in macaque visual cortex areas V1 and V4.
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Abstract
Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.
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Affiliation(s)
- Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461; .,Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Ruben Coen-Cagli
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; ,
| | - Ingmar Kanitscheider
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Center of Learning and Memory, The University of Texas at Austin, Austin, Texas 78712; .,Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627.,Gatsby Computational Neuroscience Unit, University College London, W1T 4JG London, United Kingdom
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Choi JS, Menzies RJ, Dura-Bernal S, Francis JT, Lytton WW, Kerr CC. Spiking network modeling of neuronal dynamics in individual rats. BMC Neurosci 2015. [PMCID: PMC4697585 DOI: 10.1186/1471-2202-16-s1-p122] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Zhu M, Rozell CJ. Modeling Inhibitory Interneurons in Efficient Sensory Coding Models. PLoS Comput Biol 2015; 11:e1004353. [PMID: 26172289 PMCID: PMC4501572 DOI: 10.1371/journal.pcbi.1004353] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 05/21/2015] [Indexed: 11/19/2022] Open
Abstract
There is still much unknown regarding the computational role of inhibitory cells in the sensory cortex. While modeling studies could potentially shed light on the critical role played by inhibition in cortical computation, there is a gap between the simplicity of many models of sensory coding and the biological complexity of the inhibitory subpopulation. In particular, many models do not respect that inhibition must be implemented in a separate subpopulation, with those inhibitory interneurons having a diversity of tuning properties and characteristic E/I cell ratios. In this study we demonstrate a computational framework for implementing inhibition in dynamical systems models that better respects these biophysical observations about inhibitory interneurons. The main approach leverages recent work related to decomposing matrices into low-rank and sparse components via convex optimization, and explicitly exploits the fact that models and input statistics often have low-dimensional structure that can be exploited for efficient implementations. While this approach is applicable to a wide range of sensory coding models (including a family of models based on Bayesian inference in a linear generative model), for concreteness we demonstrate the approach on a network implementing sparse coding. We show that the resulting implementation stays faithful to the original coding goals while using inhibitory interneurons that are much more biophysically plausible. Cortical function is a result of coordinated interactions between excitatory and inhibitory neural populations. In previous theoretical models of sensory systems, inhibitory neurons are often ignored or modeled too simplistically to contribute to understanding their role in cortical computation. In biophysical reality, inhibition is implemented with interneurons that have different characteristics from the population of excitatory cells. In this study, we propose a computational approach for including inhibition in theoretical models of neural coding in a way that respects several of these important characteristics, such as the relative number of inhibitory cells and the diversity of their response properties. The main idea is that the significant structure of the sensory world is reflected in very structured models of sensory coding, which can then be exploited in the implementation of the model using modern computational techniques. We demonstrate this approach on one specific model of sensory coding (called “sparse coding”) that has been successful at modeling other aspects of sensory cortex.
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Affiliation(s)
- Mengchen Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
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Solomon SS, Chen SC, Morley JW, Solomon SG. Local and Global Correlations between Neurons in the Middle Temporal Area of Primate Visual Cortex. Cereb Cortex 2014; 25:3182-96. [DOI: 10.1093/cercor/bhu111] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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10
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Stochastic computations in cortical microcircuit models. PLoS Comput Biol 2013; 9:e1003311. [PMID: 24244126 PMCID: PMC3828141 DOI: 10.1371/journal.pcbi.1003311] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2013] [Accepted: 08/22/2013] [Indexed: 12/30/2022] Open
Abstract
Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
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Herrero J, Gieselmann M, Sanayei M, Thiele A. Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors. Neuron 2013; 78:729-39. [PMID: 23719166 PMCID: PMC3748348 DOI: 10.1016/j.neuron.2013.03.029] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2013] [Indexed: 11/19/2022]
Abstract
Attention improves perception by affecting different aspects of the neuronal code. It enhances firing rates, it reduces firing rate variability and noise correlations of neurons, and it alters the strength of oscillatory activity. Attention-induced rate enhancement in striate cortex requires cholinergic mechanisms. The neuropharmacological mechanisms responsible for attention-induced variance and noise correlation reduction or those supporting changes in oscillatory activity are unknown. We show that ionotropic glutamatergic receptor activation is required for attention-induced rate variance, noise correlation, and LFP gamma power reduction in macaque V1, but not for attention-induced rate modulations. NMDA receptors mediate attention-induced variance reduction and attention-induced noise correlation reduction. Our results demonstrate that attention improves sensory processing by a variety of mechanisms that are dissociable at the receptor level.
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Affiliation(s)
- Jose L. Herrero
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Marc A. Gieselmann
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Mehdi Sanayei
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Alexander Thiele
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
- Corresponding author
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12
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Abstract
The spiking activity of nearby cortical neurons is correlated on both short and long time scales. Understanding this shared variability in firing patterns is critical for appreciating the representation of sensory stimuli in ensembles of neurons, the coincident influences of neurons on common targets, and the functional implications of microcircuitry. Our knowledge about neuronal correlations, however, derives largely from experiments that used different recording methods, analysis techniques, and cortical regions. Here we studied the structure of neuronal correlation in area V4 of alert macaques using recording and analysis procedures designed to match those used previously in primary visual cortex (V1), the major input to V4. We found that the spatial and temporal properties of correlations in V4 were remarkably similar to those of V1, with two notable differences: correlated variability in V4 was approximately one-third the magnitude of that in V1 and synchrony in V4 was less temporally precise than in V1. In both areas, spontaneous activity (measured during fixation while viewing a blank screen) was approximately twice as correlated as visual-evoked activity. The results provide a foundation for understanding how the structure of neuronal correlation differs among brain regions and stages in cortical processing and suggest that it is likely governed by features of neuronal circuits that are shared across the visual cortex.
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13
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Zhang D, Li Y, Rasch MJ, Wu S. Nonlinear multiplicative dendritic integration in neuron and network models. Front Comput Neurosci 2013; 7:56. [PMID: 23658543 PMCID: PMC3647120 DOI: 10.3389/fncom.2013.00056] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Accepted: 04/21/2013] [Indexed: 11/13/2022] Open
Abstract
Neurons receive inputs from thousands of synapses distributed across dendritic trees of complex morphology. It is known that dendritic integration of excitatory and inhibitory synapses can be highly non-linear in reality and can heavily depend on the exact location and spatial arrangement of inhibitory and excitatory synapses on the dendrite. Despite this known fact, most neuron models used in artificial neural networks today still only describe the voltage potential of a single somatic compartment and assume a simple linear summation of all individual synaptic inputs. We here suggest a new biophysical motivated derivation of a single compartment model that integrates the non-linear effects of shunting inhibition, where an inhibitory input on the route of an excitatory input to the soma cancels or “shunts” the excitatory potential. In particular, our integration of non-linear dendritic processing into the neuron model follows a simple multiplicative rule, suggested recently by experiments, and allows for strict mathematical treatment of network effects. Using our new formulation, we further devised a spiking network model where inhibitory neurons act as global shunting gates, and show that the network exhibits persistent activity in a low firing regime.
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Affiliation(s)
- Danke Zhang
- School of Automation Science and Engineering, South China University of Technology Guangzhou, China ; State Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
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14
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Dahl CD, Rasch MJ, Tomonaga M, Adachi I. Developmental processes in face perception. Sci Rep 2013; 3:1044. [PMID: 23304435 PMCID: PMC3540399 DOI: 10.1038/srep01044] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 12/05/2012] [Indexed: 11/08/2022] Open
Abstract
Understanding the developmental origins of face recognition has been the goal of many studies of various approaches. Contributions of experience-expectant mechanisms (early component), like perceptual narrowing, and lifetime experience (late component) to face processing remain elusive. By investigating captive chimpanzees of varying age, a rare case of a species with lifelong exposure to non-conspecific faces at distinctive levels of experience, we can disentangle developmental components in face recognition. We found an advantage in discriminating chimpanzee above human faces in young chimpanzees, reflecting a predominant contribution of an early component that drives the perceptual system towards the conspecific morphology, and an advantage for human above chimpanzee faces in old chimpanzees, reflecting a predominant late component that shapes the perceptual system along the critical dimensions of the face exposed to. We simulate the contribution of early and late components using computational modeling and mathematically describe the underlying functions.
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Affiliation(s)
- Christoph D. Dahl
- Primate Research Institute, Kyoto University, Section Language and Intelligence, 41-2 Kanrin, Inuyama, Aichi, 484-8506, Japan
| | - Malte J. Rasch
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Xinjiekouwai Street 19, 100875 Beijing, China
| | - Masaki Tomonaga
- Primate Research Institute, Kyoto University, Section Language and Intelligence, 41-2 Kanrin, Inuyama, Aichi, 484-8506, Japan
| | - Ikuma Adachi
- Primate Research Institute, Kyoto University, Section Language and Intelligence, 41-2 Kanrin, Inuyama, Aichi, 484-8506, Japan
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15
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Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. ACTA ACUST UNITED AC 2012. [PMID: 23203991 PMCID: PMC3920768 DOI: 10.1093/cercor/bhs358] [Citation(s) in RCA: 200] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While available comprehensive connectivity maps (
Thomson, West, et al. 2002; Binzegger et al. 2004) have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we analyze the properties of these maps to compile an integrated connectivity map, which additionally incorporates insights on the specific selection of target types. Based on this integrated map, we build a full-scale spiking network model of the local cortical microcircuit. The simulated spontaneous activity is asynchronous irregular and cell-type specific firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. The interplay of excitation and inhibition captures the flow of activity through cortical layers after transient thalamic stimulation. In conclusion, the integration of a large body of the available connectivity data enables us to expose the dynamical consequences of the cortical microcircuitry.
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Affiliation(s)
- Tobias C Potjans
- Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Juelich, Juelich, Germany
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16
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Smith MA, Jia X, Zandvakili A, Kohn A. Laminar dependence of neuronal correlations in visual cortex. J Neurophysiol 2012. [PMID: 23197461 DOI: 10.1152/jn.00846.2012] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30-50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.
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Affiliation(s)
- Matthew A Smith
- Department of Ophthalmology, Center for Neural Basis of Cognition and Fox Center for Vision Restoration, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA.
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17
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Information coding in a laminar computational model of cat primary visual cortex. J Comput Neurosci 2012; 34:273-83. [PMID: 22907135 DOI: 10.1007/s10827-012-0420-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 07/21/2012] [Accepted: 07/29/2012] [Indexed: 10/28/2022]
Abstract
Neural populations across cortical layers perform different computational tasks. However, it is not known whether information in different layers is encoded using a common neural code or whether it depends on the specific layer. Here we studied the laminar distribution of information in a large-scale computational model of cat primary visual cortex. We analyzed the amount of information about the input stimulus conveyed by the different representations of the cortical responses. In particular, we compared the information encoded in four possible neural codes: (1) the information carried by the firing rate of individual neurons; (2) the information carried by spike patterns within a time window; (3) the rate-and-phase information carried by the firing rate labelled by the phase of the Local Field Potentials (LFP); (4) the pattern-and-phase information carried by the spike patterns tagged with the LFP phase. We found that there is substantially more information in the rate-and-phase code compared with the firing rate alone for low LFP frequency bands (less than 30 Hz). When comparing how information is encoded across layers, we found that the extra information contained in a rate-and-phase code may reach 90 % in Layer 4, while in other layers it reaches only 60 %, compared to the information carried by the firing rate alone. These results suggest that information processing in primary sensory cortices could rely on different coding strategies across different layers.
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18
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Abstract
To understand computations in neuronal circuits, a model of a small patch of cortex has been developed that can describe the firing regime in the visual system remarkably well.
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Affiliation(s)
- William S Anderson
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
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19
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Abstract
Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.
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Affiliation(s)
- Marlene R Cohen
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA.
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