How simple cells are made in a nonlinear network model of the visual cortex

Simple cells in the striate cortex respond to visual stimuli in an approxim ately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vi tal for visual perception, there has been no satisfactory explanation of ho w they are produced in the cortex. To examine this question, we have develo ped a large-scale neuronal network model of layer 4C alpha in V1 of the mac aque cortex that is based on, and constrained by, realistic cortical anatom y and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. (2) To identify how the model generates th is linearized response in a nonlinear network. Each neuron in the model rec eives nonlinear excitation from the lateral geniculate nucleus (LGN). The c ells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of mem brane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear e xcitatory input from the LGN. This interaction produces linearized response s that agree with both extracellular and intracellular measurements. The mo del correctly accounts for experimental results about the time course of si mple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excita tion and inhibition pharmacologically or with extrinsic current.