Botond Roska

A qualitative model-framework for spatio-temporal effects in vertebrate retinas

Retinal models based on the cellular neural network (CNN) paradigm have been widely used. These neuromorphic models are based on retinal anatomy and physiology. In this paper a framework is proposed for qualitative spatio-temporal studies in vertebrate retinas, the underlying retinal anatomy is followed as closely as possible, the characteristics of the physiological models, however, are kept simple. The goal is to model the qualitative effects, since the developed models are simple, compared to a fully neuromorphic one, we have a good chance to implement them on CNN Universal Machine chips using multi-layer technology.

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

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.