Proceedings PaperLinear responses to nonlinear signals: a neural network model of spatiotemporal visual processing
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The initial transformation of light into neural signals is known to introduce nonlinearities in the spatiotemporal responses of retinal cells. In spite of these early nonlinearities, at least one class of retinal ganglion cells (the X cells first reported by Enroth Cugell & Robson) behaves as if all processing prior to the ganglion cell layer were linear. Similarly, frequency analyses show that cortical simple and complex cells are largely unaffected by well-known nonlinearities in the ganglion cell output. A push-pull model of retinal processing can reconcile these paradoxes by showing how ganglion cells can be selectively tuned to transient or sustained components of their input signals, independently of contrast or average retinal illuminance, and in spite of arbitrary nonlinear preprocessing. Theoretical considerations suggest that similar push-pull connectivity should also exist in the pathway joining ganglion cells to visual cortex. This model differs from other push-pull mechanisms in that each cell is described by nonlinear membrane equations, but response is linearized by the convergence of push-pull inputs.