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通过统计复杂度测量来检测神经网络中的随机多重共振

Yazhen Wu^1,2, Zhongkui Sun³

¹School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, 710129, China.

Scientific reports

|March 4, 2024

概括

统计复杂度测量揭示了神经网络中的四倍随机共振. 时间延迟诱导多重共振,通过优化网络参数来增强下值信号检测.

科学领域:

神经科学是一个神经科学.
复杂的系统复杂的系统.
信号处理信号处理

背景情况:

随机多重共振 (SMR) 是一种在非线性系统中噪声增强信号检测的现象.
小世界神经网络和FitzHugh-Nagumo (FHN) 神经元是用于研究复杂神经元动态的模型.
信息传输的时间延迟可以显著影响神经元网络的行为.

研究的目的:

调查小世界FHN神经网络中由噪声和时间延迟引起的SMR的发生.
分析统计复杂度测量 (SCM) 在识别SMR中的作用.
探索网络参数和时间延迟如何影响信号检测.

主要方法:

使用统计复杂度测量 (SCM) 来量化系统复杂度.
模拟小世界神经网络与FitzHugh-Nagumo (FHN) 神经元相结合.
分析不同噪音水平和时间延迟对神经元发射动态的影响.

主要成果:

在四个最佳噪音水平上,SCM发现了四倍的随机共振.
在中等噪音下,观察到延迟诱导的SMR是在下值信号周期的倍数.
时间延迟和神经元触发之间的一致性是在低噪音下出现的特定延迟.
优化网络参数 (程度,大小,合强度) 增强了延迟诱导的SMR.

结论:

SCM是检测SMR和理解神经网络中其机制的有效工具.
时间延迟在诱导多共振现象中起着至关重要的作用.
网络参数调整可以优化SMR,以改善下值信号检测和信息传输.

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