Alvin Yao

Impact Factor:3.5

DOI number:10.1504/IJBIC.2023.10054455

Journal:International Journal of Bio-Inspired Computation

Place of Publication:ENGLAND

Key Words:Fractional-order systems; Neural network motifs; Discrete LIF model; Chaotic resonance; Dynamic behaviour

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.

Indexed by:Article

Discipline:Engineering

Document Type:J

Volume:21

Issue:4

Page Number:175-188

ISSN No.:1758-0366

Translation or Not:no

Date of Publication:2023-08-04

Included Journals:SCI(E)

Links to published journals:https://www.inderscienceonline.com/doi/pdf/10.1504/IJBIC.2023.132777