期刊
PHYSICS LETTERS A
卷 492, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physleta.2023.129232
关键词
Neural network; Mean-field equations; Synchronization control; Quadratic integrate-and-fire neurons; Hindmarsh-Rose neurons
This study investigates the suppression control of unstable equilibrium states and coherent oscillations in large-scale neural networks. A first-order dynamic controller is implemented to stabilize unknown equilibrium states and successfully suppress coherent oscillations.
In large-scale neural networks, coherent limit cycle oscillations usually coexist with unstable incoherent equilibrium states, which are not observed experimentally. We implement a first-order dynamic controller to stabilize unknown equilibrium states and suppress coherent oscillations. The stabilization of incoherent equilibria associated with unstable focus and saddle is considered. The algorithm is demonstrated for networks composed of quadratic integrate-and-fire (QIF) neurons and Hindmarsh-Rose neurons. The microscopic equations of an infinitely large QIF neural network can be reduced to an exact low-dimensional system of mean-field equations, which makes it possible to study the control problem analytically.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据