This chapter reviews our approach to large-scale computational modeling of the primary visual cortex (V1). The main objectives of our modeling are to (i) capture groups of experimentally observed phenomena in a single theoretical model of cortical circuitry, and (ii) identify the physiological mechanisms underlying the model dynamics. We have achieved these goals 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 (i) high total conductance, (ii) strong inhibition, (iii) large synaptic fluctuations, (iv) an important role of NMDA conductance in the orientation-specific, long-range interactions, and (v) 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 to identify and investigated model neuronal network mechanisms underlying cortical phenomena such including (i) spatiotemporal patterns of spontaneous cortical activity, (ii) cortical activity patterns induced by the Hikosaka line-motion illusion stimulus paradigm, (iii) membrane potential synchronization in nonspiking neurons several millimeters apart, and (iv) neuronal orientation tuning in V1.
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This work was partly supported by the National Science Foundation through grant DMS-0506287.
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