The network behind spatio‐temporal patterns: building low‐complexity retinal models in CNN based on morphology, pharmacology and physiology

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Abstract
In this paper, a vertebrate retina model is described based on a cellular neural network (CNN) architecture. Though largely built on the experience of previous studies, the CNN computational framework is considerably simplified: first‐order RC cells are used with space‐invariant nearest‐neighbour interactions only. All non‐linear synaptic connections are monotonic continuous functions of the pre‐synaptic voltage. Time delays in the interactions are continuous represented by additional first‐order cells. The modelling approach is neuromorphic in its spirit relying on both morphological and pharmacological information. However, the primary motivation lies in fitting the spatio‐temporal output of the model to the data recorded from biological cells (tiger salamander). In order to meet a low‐complexity (VLSI) implementation framework some structural simplifications have been made. Large‐neighbourhood interaction (neurons with large processes), furthermore inter‐layer signal propagation are modelled through diffusion and wave phenomena. This work presents novel CNN models for the outer and some partial models for the inner (light adapted) retina. It describes an approach that focuses on efficient parameter tuning and also makes it possible to discuss adaptation, sensitivity and robustness issues on retinal ‘image processing’ from an engineering point of view. Copyright © 2001 John Wiley & Sons, Ltd.

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