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Large-Scale Computational Modeling of the Primary Visual Cortex

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Coherent Behavior in Neuronal Networks

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 3))

Abstract

This chapter reviews our approach to large-scale computational modeling of the primary visual cortex (V1). The main objectives of our modeling are to (1) capture groups of experimentally observed phenomena in a single theoretical model of cortical circuitry, and (2) identify the physiological mechanisms underlying the model dynamics. We have achieved these objectives by building parsimonious models based on minimal, yet sufficient, sets of anatomical and physiological assumptions. We have also verified the structural robustness of the proposed network mechanisms. During the modeling process, we have identified a particular operating state of our model cortex from which we believe that V1 responds to changes in visual stimulation. This state is characterized by (1) high total conductance, (2) strong inhibition, (3) large synaptic fluctuations, (4) an important role of NMDA conductance in the orientation-specific, long-range interactions, and (5) a high degree of correlation between the neuronal membrane potentials, NMDA-type conductances, and firing rates. Tuning our model to this operating state in the absence of stimuli, we have used it to identify and investigate model neuronal network mechanisms underlying cortical phenomena including (1) spatiotemporal patterns of spontaneous cortical activity, (2) cortical activity patterns induced by the Hikosaka line-motion illusion stimulus paradigm, (3) membrane potential synchronization in nonspiking neurons several millimeters apart, and (4) neuronal orientation tuning in V1.

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Acknowledgment

A.V.R. and D.C. were partly supported by the NSF grant DMS-0506396 and by the Schwartz foundation. L.T. was supported by the NSF grant DMS-0506257. G.K. was supported by NSF grants IGMS-0308943 and DMS-0506287, and gratefully acknowledges the hospitality of the Courant Institute of Mathematical Sciences and Center for Neural Science during his visits at New York University in 2003/04 and 2008.

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  1. Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY, 10012, USA

    David Cai

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  1. Aaditya V. Rangan
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  2. Louis Tao
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  3. Gregor Kovačič
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  4. David Cai
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Correspondence to David Cai .

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Editors and Affiliations

  1. Dept. Mathematics, University of Houston, Phillip G. Hoffman Hall 651, Houston, 77204-3008, U.S.A.

    Kre¿imir Josic

  2. Dept. Mathematics, University of Pittsburgh, Thackeray Hall 301, Pittsburgh, 15260, U.S.A.

    Jonathan Rubin

  3. Universitat de les Illes Balears, CSIC, Palma de Mallorca, 07122, Spain

    Manuel Matias

  4. Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico, 04510, Mexico

    Ranulfo Romo

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Rangan, A.V., Tao, L., Kovačič, G., Cai, D. (2009). Large-Scale Computational Modeling of the Primary Visual Cortex. In: Josic, K., Rubin, J., Matias, M., Romo, R. (eds) Coherent Behavior in Neuronal Networks. Springer Series in Computational Neuroscience, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0389-1_14

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