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Large-Scale Computational Modeling of the Primary Visual Cortex

  • Aaditya V. Rangan
  • Louis Tao
  • Gregor Kovačič
  • David Cai
Chapter
Part of the Springer Series in Computational Neuroscience book series (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.

Keywords

Firing Rate Lateral Geniculate Nucleus Orientation Tuning Network Mechanism Spatiotemporal Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Aaditya V. Rangan
  • Louis Tao
  • Gregor Kovačič
  • David Cai
    • 1
  1. 1.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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