Path-integral evolution of chaos embedded in noise: Duffing neocortical analog

TL;DR

This paper models neocortical chaos using a noisy Duffing oscillator and employs a path-integral algorithm to analyze whether chaos persists under realistic neural noise levels.

Contribution

It introduces a novel application of path-integral methods to study chaos in a noisy neural model, bridging nonlinear dynamics and realistic brain noise conditions.

Findings

Chaos persists in the model under moderate noise levels.

Path-integral approach effectively handles nonlinear stochastic systems.

Potential for studying chaos survival in biological neural networks.

Abstract

A two dimensional time-dependent Duffing oscillator model of macroscopic neocortex exhibits chaos for some ranges of parameters. We embed this model in moderate noise, typical of the context presented in real neocortex, using PATHINT, a non-Monte-Carlo path-integral algorithm that is particularly adept in handling nonlinear Fokker-Planck systems. This approach shows promise to investigate whether chaos in neocortex, as predicted by such models, can survive in noisy contexts.