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

被引:0
作者
Fennelly, N. [1 ]
Neff, A. [2 ]
Lambiotte, R. [3 ]
Keane, A. [4 ]
Byrne, A. [1 ]
机构
[1] Univ Coll Dublin, Sch Math & Stat, Belfield 4, Ireland
[2] Univ Edinburgh, Sch Math, Edinburgh EH9 3FD, Scotland
[3] Univ Oxford, Math Inst, Oxford OX2 6GG, England
[4] Univ Coll Cork, Sch Math Sci, Cork T12 XF62, Ireland
基金
英国工程与自然科学研究理事会; 爱尔兰科学基金会;
关键词
GENERATION NEURAL MASS; MACROSCOPIC BEHAVIOR; SYNAPTIC PLASTICITY; MODEL;
D O I
10.1063/5.0231457
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Synaptic plasticity plays a fundamental role in neuronal dynamics, governing how connections between neurons evolve in response to experience. 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 explore two distinct implementations of this plasticity: pairwise updates to individual coupling strengths and global updates applied to the mean coupling strength. We derive a mean-field approximation and assess its accuracy by comparing it to theta-neuron simulations across various stability regimes. The synchrony of the system is quantified using the Kuramoto order parameter. Through bifurcation analysis and the calculation of maximal Lyapunov exponents, we uncover interesting phenomena such as bistability and chaotic dynamics via period-doubling and boundary crisis bifurcations. These behaviors emerge as a direct result of adaptive coupling and are absent in systems without such plasticity.
引用
收藏
页数:13
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