Averaging Transformations of Synaptic Potentials on Networks

TL;DR

This paper introduces a mathematical model that simulates how microscopic synaptic potentials are transformed into macroscopic signals in brain networks, validated on the striatal complex of the basal ganglia.

Contribution

A novel phenomenological model for transforming microscopic synaptic potentials into macroscopic brain signals, validated on the striatal complex.

Findings

Model successfully simulates synaptic integration in brain compartments.

Validation on the striatal complex supports model's relevance.

Provides insights into information processing in neural networks.

Abstract

The problem of the transformation of microscopic information to the macroscopic level is an intriguing challenge in computational neuroscience, but also of general mathematical importance. Here, a phenomenological mathematical model is introduced that simulates the internal information processing of brain compartments. Synaptic potentials are integrated over small number of realistically coupled neurons to obtain macroscopic quantities. The striatal complex, an important part of the basal ganglia circuit in the brain for regulating motor activity, has been investigated as an example for the validation of the model.