Identification of complex-cell intensive nonlinearities in a cascade model of cat visual cortex

Complex cells in the cat's visual cortex show nonlinearities in processing of image luminance and movement. To study mechanisms, initally we have represented the chain of neurons from retina to cortex as a black-box model. Independent information about the visual system has helped us cast this Wiener-kernel model into a dynamic-linear/static-nonlinear/dynamiclinear (LNL) cascade. We then use system identification techniques to define the nature of these transformations directly from responses of the neuron to a single presentation of a stimulus composed of a sequence of white-noise-modulated luminance values. The two dynamic linear filters are mainly low-pass, and the static nonlinearity is mainly of even polynomial degree. This approximate squaring function may be effected in the animal by soft-thresholding each of the linear ON- and OFF-channel signals and then summing them, which account for ON-OFF responses and for the squaring operation needed for computation of motion energy, both observed in these neurons.