Dependence of Visual Cell Properties on Intracortical Synapses Among Hypercolumns: Analysis by a Computer Model

The role of intracortical synapses in affecting the property of visual cells is investigated by means of an original mathematical model of cortical circuitry in V1. The model represents a compromise between computational simplicity and physiological reliability. The model incorporates four different inputs into a cortical cell: thalamic input from the lateral geniculate nucleus, according to an even Gabor function; short-range inhibition confined within the hypercolumn; a long-range excitation, which emphasizes the properties of the input; and a long-range inhibition. In the model we assume that all cells receive a similar thalamic input, which differs simply according to its position in the retina and orientation preference. Simulations were performed, with different parameter values, to assess the main characteristics of cell response (i.e., the width and locations of subregions in the receptive field (RF), orientation tuning curve, and response to drifting and counterphase gratings) as a function of the strength and extension of intracortical excitatory synapses. Results suggest that, if intracortical excitation is confined within the hypercolumn, the cells exhibit the same properties as simple cells, both with regards to the width and shape of the RF, orientation tuning curve, and response to drifting and counterphase gratings. By contrast, if excitatory synapses extend beyond the hypercolumn with sufficient strength, the cells exhibit the typical characteristics of complex cells. A progressive shift from complex to simple cells can be realized with a monotonic variation in parameters. Simulations are also performed with a hierarchical model, to suggest possible experiments able to discriminate the present recurrent mechanism from the classical hierarchical one. Results support the assumptions of previous simpler models (Chance et al., 1999) and may help to understand and assess the role of intracortical synapses in rigorous quantitative terms.