A COMPUTATIONAL STUDY OF THE DIFFUSE NEIGHBOURHOODS IN BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS

This paper presents a computational study on a fundamental aspect concerning with the dynamic of nitric oxide (NO) both in the biological and artificial neural networks, the Diffuse Neighbourhood (DNB). We apply the compartmental model of NO diffusion as formal tool, using a computational neuroscience point of view. The main objective is the analysis of DNB by the observation of the AI-NOD and CDNB variables, defined in this work. We present a study of influences and dependences with respect to associated features to the NO synthesis-diffusion process, and to the environment where it spreads (non-isotropy and non-homogeneity). It is structured into three sets of experiences which cover the quoted aspects: influence of the NO synthesis process, isolated and multiple processes, influence of distance to the element where NO is synthesized, influence of features of the diffusion environment. The developments have been performed in mono and bi-dimensional environments, with endothelial cell features. The importance of this study is providing the needed formalism to quantify the information representation capacity that a type of NO diffusion-based signalling presents and their implications in many other underlying neural mechanisms as neural recruitment, synchronization of computations between neurons and in the brain activity in general.