Coherence resonance and stochastic synchronization in a small-world neural network: An interplay in the presence of spike-timing-dependent plasticity

TL;DR

This study investigates how specific network structures and STDP parameters can simultaneously enhance coherence resonance and stochastic synchronization in a small-world neural network, revealing optimal conditions for neural information processing.

Contribution

It identifies the conditions under which network topology and STDP parameters jointly optimize coherence resonance and synchronization in Hodgkin-Huxley neuron networks.

Findings

CR and SS are maximized in specific network and STDP parameter ranges.

Optimal background noise, STDP rule, and topology improve neural firing precision.

Results suggest a constructive role of noise and plasticity in neural dynamics.

Abstract

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at which CR and SS are both pronounced is a fundamental question of interest that is still elusive. We expect such a configuration to enable the brain to make synergistic and optimal use of these phenomena to process information efficiently. This paper considers a small-world network of excitable Hodgkin-Huxley neurons driven by channel noise and STDP with an asymmetric Hebbian time window. Numerical results indicate specific network topology and STDP parameter intervals in which CR and SS can be simultaneously enhanced. Our results imply that an optimally tuned inherent background noise, STDP rule, and network topology can play a constructive role in enhancing both the time precision of firing and the synchronization in neural systems.