Stochastic resonance: The response to envelope modulation signal for neural networks with different topologies

Huixia Liu, Lulu Lu, Yuan Zhu, Zhouchao Wei and Ming Yi

Physica A: Statistical Mechanics and its Applications, 2022, vol. 607, issue C

Abstract: Neurons receive complex multiple signals from different regions in the biology system, and the neuron information carried by the multi-frequency signals can be detected by the neural system. Noise can induce stochastic resonance and promote the response of the neural network to a weak signal. The responses to weak envelope modulation signals for the neural networks with different topologies are investigated under electromagnetic induction and Gaussian white noise. We analyze and compare the stochastic resonance with tuning system parameters. The results show that in the three different networks, network systems exhibit double resonance with the variation of noise and beat frequency. Appropriate noise and electromagnetic induction can promote the generation of stochastic resonance and affect the response of envelope modulation signal in the neural network. The difference is that the three different networks differ in the noise intensity required to induce stochastic resonance and the effective range of the electrical coupling strength. Unlike the other two networks, the Fourier coefficient in scale-free network increases with the electromagnetic induction, and the number of neurons has no affect on stochastic resonance. Our results are conducive to understanding the mechanism of stochastic resonance in envelope modulation signals and electromagnetic induction.

Keywords: Stochastic resonance; Envelope modulation signal; Electromagnetic induction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:607:y:2022:i:c:s037843712200735x

DOI: 10.1016/j.physa.2022.128177

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