Volume Signaling and Neural-Indexing by Nitric Oxide in Artificial Neural Networks

Abstract

We present a computational study whose objective is to show the capacity of the Nitric Oxide (NO) diffusion for information recovery and indexing related to the classical neural architecture the Sparse Distributed Memory (SDM) has. The study is carried out by introducing NO diffusion dynamics by means of a Multi-compartment based NO Diffusion Model, in the storage process of the SDM. We develop a new SDM model, which we term Sparse Distributed Memory by Nitric Oxide diffusion (SDM-NO). Both of these architectures were computationally analysed. We have showed that the information indexing guided by the Nitric Oxide dynamics has a similar or slightly better behavior to the randomly guided by the SDM. For this study we have used two kinds of patterns, a) binary string patterns with eight bits and b) handwritten characters, that the indexing guided by the Nitric Oxide dynamics shows a similar or a little bit better behaviour to the guided indexing one performed randomly by the SDM. Nevertheless, we have also shown that both of the architectures do not perform well in these memory processes.