Resonance dynamics in multilayer neural networks subjected to electromagnetic induction

This work focuses on investigating multiple-stochastic resonances (MSRs) in a multilayer neural network composed of delay-coupled FitzHugh-Nagumo (FHN) neurons under electromagnetic induction. Statistical complexity measure (SCM) has been defined and calculated in this model based on its normalized Shannon-entropy (NSE), allowing for the detection and characterization of MSRs. Numerical results reveal that moderate inter-layer coupling strength promotes resonance effects synchronously at both mesoscale and macroscale, despite the differences in inter-layer network structures. We also demonstrate that noise can induce stochastic resonance (SR) up to ⌊T_e/T₀⌋ times in this multilayer network, where T_e represents the period of subthreshold signal (STS) and T₀ denotes the noise-induced mean firing period. Furthermore, we observe that noise-induced MSRs remain nearly unaffected as feedback gain increases, indicating their robustness to electromagnetic induction. Besides, a clear optimal feedback gain is identified, which maximizes the strength of fourth noise-induced SR. Moreover, an increase in feedback gain enhances the delay-induced MSRs for moderate time delays, while it slightly restrains the delay-induced MSRs for larger time delays. This study provides a more effective tool than traditional indicators for understanding weak signals detection and information propagation in realistic neural systems.