Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus

Abstract

Recently, several mathematical models have been developed to study and explain the way information isprocessed in the brain. The models published account for a myriad of perspectives from single neuron segments toneural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nucleiinteracting for massive stimuli and processing. Some of the most complex neural structures -and also moststudied- are basal ganglia nuclei in the brain; amongst which we can find the Neostriatum. Currently, just a fewpapers about high scale biological-based computational modeling of this region have been published. It has beendemonstrated that the Basal Ganglia region contains functions related to learning and decision making based onrules of the action-selection type, which are of particular interest for the machine autonomous-learning field. Thisknowledge could be clearly transferred between areas of research. The present work proposes a model ofinformation processing, by integrating knowledge generated from widely accepted experiments in both morphologyand biophysics, through integrating theories such as the compartmental electrical model, the Rall’s cable equation,and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework isincorporated in an adapted function. This was accomplished through a computational environment prepared forhigh scale neural simulation which delivers data output equivalent to that from the original model, and that can notonly be analyzed as a Bayesian problem, but also successfully compared to the biological specimen.