Building on prior research introducing the discrete fractional-order Izhikevich neuron (DFOIN) model, this study investigates its role in small-world networks to analyse the impact of fractional plasticity on chaotic resonance. By incorporating a fractional-order plasticity mechanism, we address phase misalignment between pacemaker neurons and the network, which arises due to communication delays. We use Fourier coefficients to evaluate the network's ability to detect weak signals and systematically examine the effects of small-world network rewiring probability and fractional-order dynamics on chaotic resonance. Our findings show that fractional plasticity improves signal detection and network adaptability by dynamically adjusting fractional orders. This study highlights the synergy between small-world network structures and fractional plasticity, providing a novel framework for optimising network behaviour in real-time. The results have potential applications in neuromorphic computing and biologically inspired network design.
Yin et al. (Thu,) studied this question.