A biological plausible recurrent model of V1 hypercolumns
© Afşar et al; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
A biological plausible model of hypercolumn of V1 layer in the Primary Visual Cortex, modeled in the NEST Environment , is presented. The model addresses experimental findings on emergence of orientation selectivity which occurs in the V1 . The network model is derived from the Bayesian confidence propagation neural network, which was presented earlier [3, 4]. It is hypothesized that a modular recurrent network model can be used to address orientation selectivity mechanism . Thus, the columnar organization of primary visual cortex is assumed . The network consists of 16 minicolumn models each representing an orientation, ranging from 0o to 168.75o, with the angular distance of 11.25o between two adjacent minicolumn models. LGN input is broadly tuned, half-width of half-height (HWHH) is 40 o. Excitatory->Excitatory network targets all neurons with the probability of 60% inside the host minicolumn with a HWHH of 25o as a function of distance (ESPSs = 3.15 mV). Inhibitory->Excitatory is connected with the probability of 40% (ISPSs = -5.85mV). Excitatory->Inhibitory connections target all neurons with the probability of 40%, and HWHH of 67.5o as a function of distance (ESPSs = 1.35 mV). Furthermore, LGN input is 1/3 of cortical excitation. Hypercolumn model also reflects biological phenomenon of background activity caused by random cortical inputs, as suggested by the experimental findings. In the absence of LGN input, background activity of the population is around 0.5-2 spikes/sec.
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