Initial conditions in the neural field model

TL;DR

This paper reviews how initial conditions influence the dynamics of neural field models, highlighting their effects on system behavior, stability, and brain function, with insights into irreversibility and pathological states.

Contribution

It provides a systematic overview of the impact of initial conditions on neural models, integrating concepts like phase space and bifurcations to enhance understanding.

Findings

Initial conditions significantly affect neural dynamics.

Analysis of irreversibility relates to brain function.

Insights into normal and pathological brain states.

Abstract

In spite of the large amount of existing neural models in the literature, there is a lack of a systematic review of the possible effect of choosing different initial conditions on the dynamic evolution of neural systems. In this short review we intend to give insights into this topic by discussing some published examples. First, we briefly introduce the different ingredients of a neural dynamical model. Secondly, we introduce some concepts used to describe the dynamic behavior of neural models, namely phase space and its portraits, time series, spectra, multistability and bifurcations. We end with an analysis of the irreversibility of processes and its implications on the functioning of normal and pathological brains.