Title: Chaotic resonance in discrete fractional-order LIF neural network motifs

Authors: Hao Yin; Jiacheng Tang; Bo Liu; Shuaiyu Yao; Qi Kang

Addresses: Department of Control Science and Engineering, Tongji University, Shanghai, China ' Department of Control Science and Engineering, Tongji University, Shanghai, China ' Informatization Office, Shanghai University of Traditional Chinese Medicine, Shanghai, China ' Department of Control Science and Engineering, Tongji University, Shanghai, China ' Department of Control Science and Engineering, Tongji University, Shanghai, China

Abstract: Chaotic resonance (CR) is a phenomenon where nonlinear systems enhance respond to weak signals under the influence of chaotic signals. It exists robustly in nature, including the human nervous system. Can we build a neural network model that can detect weak signals with multiple frequencies under chaotic signals? Note that fractional calculus can naturally capture intrinsic phenomena in complex dynamical. We first introduced fractional calculus and proposed the discrete fractional-order LIF model. The triple-neuron feed-forward loop network motifs are also established. The proposed model has rich response characteristics and can better detect weak signals of various frequencies in the environment. The experimental results show that neuron and neural network motifs can independently respond to a weak signal with a certain frequency by adjusting the fractional order, and network motifs can achieve orderly cluster discharge. This provides a new idea for us to build deeper spiking neural networks and explore the mechanisms of weak signal detection and transmission in biological nervous systems.

Keywords: fractional-order systems? neural network motifs? discrete LIF model? chaotic resonance? dynamic behaviour.

DOI: 10.1504/IJBIC.2023.132777

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.4, pp.175 - 188

Received: 06 Sep 2022
Accepted: 26 Oct 2022
Published online: 09 Aug 2023 *

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