Neuromorphic CNN models for spatio-temporal effects measured in the inner and outer retina of tiger salamander

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Abstract
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 neighbor interactions only. All nonlinear 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 modeling 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 and large neighborhood interaction (neurons with large processes), furthermore the inter-layer signal propagation are modeled through diffusion and wave phenomena. This work presents novel CNN models for the outer and some partial models for the inner (light adopted) retina.

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