A CNN framework for modeling parallel processing in a mammalian retina

Abstract

Abstract We present here a simple multi‐layer cellular neural/non‐linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN‐UM) chips. The basis of the model is a simple multi‐layer cellular neural/non‐linear Network ( IEEE Trans. Circuits Systems 1988; 35 :1257; IEEE Trans. Circuits Systems 1993; 40 :147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different ‘feature detecting’ parallel representations ( Nature 2001; 410 :583–587). Our CNN model is capable of reproducing qualitatively the same full set of space–time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models. Copyright © 2002 John Wiley & Sons, Ltd.