4.6 Article

Spike-timing-dependent plasticity enhances chaotic resonance in small-world network

出版社

ELSEVIER
DOI: 10.1016/j.physa.2022.128069

关键词

Small-world network; Chaotic resonance; Spike-timing-dependent plasticity; Izhikevich neuron model

资金

  1. National Natural Science Foundation of China [12175080]

向作者/读者索取更多资源

This study investigates the effects of Spike-timing-dependent plasticity (STDP) on chaotic resonance (CR) in a small-world network. The research shows that moderately strong chaotic current inputs enhance CR due to an increase in average coupling strength after STDP learning. The study also reveals that networks with weaker coupling strengths exhibit better plasticity in response to weak signals. Furthermore, adjusting STDP windows can promote or suppress CR, and the difference in area between long-time depression and long-time potentiation windows is closely related to average coupling strength after STDP learning.
Weak signals can be detected by a nonlinear system with an appropriate chaotic current input, known as the chaotic resonance (CR). Based on Watts-Strogatz small-world network, the effects of Spike-timing-dependent plasticity (STDP) on CR were systematically investigated. Numerical simulations showed that, under moderately strong chaotic current inputs, CR is enhanced due to the increase in average coupling strength after STDP learning. The above-mentioned phenomenon was observed even with changes in frequency and the network topology. For networks with different initial coupling strengths, their responses to weak signals after STDP learning are almost the same, indicating that networks with weaker coupling strengths have better plasticity. In addition, the effects of adjusting STDP windows on CR is also investigated. It was found that CR is promoted by STDP with a relatively larger long-time potentiation window, while, suppressed by a STDP with a relatively larger long-time depression window. Also, the area difference between long-time depression and long-time potentiation windows is highly correlated with average coupling strength after STDP learning. These conclusions might provide novel insights into weak signal detection and information transmission in different adaptive neural networks. (C) 2022 Elsevier B.V. All rights reserved.

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。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据