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A dynamic model based on electrophysiological findings is presented for information processing in the visual system. The visual system behaves as an optimal encoder both of information perturbed by Poisson noise at low luminances and of noise-distorted images at suprathreshold level. The basic elements of the model, are: (1) a first layer of square-root scalers mainly performing noise reduction, where each scaler consists of two leaky integrators and a comparator; (2) a layer containing a light detector consisting of one leaky integrator and a comparator, and an increment/decrement detector consisting of two leaky integrators and a comparator. The results of simulations of threshold behavior are given. The authors show that all generally known psychophysical facts can be described with this model. When spatial interaction between neighboring basic elements is introduced, the effects of these interactions spread over a large area, thus changing properties of the total network. So far, this extensive effect has only been proved with phenomenological models. Possible applications of this model in image processing are proposed.