Mean-field approximation for networks with synchrony-driven adaptive coupling

TL;DR

This paper develops a mean-field approximation for a network of $ heta$-neurons with adaptive plasticity, revealing complex dynamics like bistability and chaos that emerge from phase-dependent coupling adjustments.

Contribution

It introduces a realistic phase-difference-dependent plasticity rule into $ heta$-neuron networks and derives a mean-field model validated against simulations.

Findings

Mean-field approximation accurately predicts network dynamics.

Adaptive plasticity induces bistability and complex bifurcations.

Evidence of chaos and boundary crises in the network dynamics.

Abstract

Synaptic plasticity is a key component of neuronal dynamics, describing the process by which the connections between neurons change in response to experiences. In this study, we extend a network model of $\theta$-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of $\theta$-neuron oscillators. We investigate two approaches for implementing this plasticity: pairwise coupling strength updates and global coupling strength updates. A mean-field approximation of the system is derived and we investigate its validity through comparison with the $\theta$-neuron simulations across various stability states. The synchrony of the system is examined using the Kuramoto order parameter. A bifurcation analysis, by means of numerical continuation and the calculation of maximal Lyapunov exponents, reveals interesting phenomena, including bistability and evidence of period-doubling and boundary crisis routes to chaos, that would otherwise not exist in the absence of adaptive coupling.