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

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 Sparse Distributed Memory (SDM). 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 shown that the information indexing guided by the Nitric Oxide dynamics has a similar or slightly better behaviour to the randomly guided indexing by the SDM. Two kinds of patterns were used in the study: a) binary string patterns with eight bits and b) handwritten characters. 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.