Coherent Response in a Chaotic Neural Network

TL;DR

This paper demonstrates that a signal-driven chaotic neural network can enhance weak signals and achieve higher coherence than traditional stochastic models, revealing potential for improved neural information processing.

Contribution

It introduces a novel chaotic neural network scheme that enhances weak signals and coherence, contrasting with conventional stochastic models like Hopfield networks.

Findings

Chaotic neural networks can amplify subthreshold signals.

They exhibit higher coherence between stimulus and response.

Compared to stochastic models, they show improved signal processing capabilities.

Abstract

We set up a signal-driven scheme of the chaotic neural network with the coupling constants corresponding to certain information, and investigate the stochastic resonance-like effects under its deterministic dynamics, comparing with the conventional case of Hopfield network with stochastic noise. It is shown that the chaotic neural network can enhance weak subthreshold signals and have higher coherence abilities between stimulus and response than those attained by the conventional stochastic model.