Role of pairwise and higher-order interactions in the inverse stochastic resonance

Xuening Li, Hong He, Wende Tang, Yiju Peng, Ya Jia and Lijian Yang

Chaos, Solitons & Fractals, 2026, vol. 209, issue P1

Abstract: Inverse stochastic resonance (ISR) is a phenomenon in neuronal systems where the mean firing rate exhibits a minimum relative to noise. Recent studies indicate that higher-order interactions play a crucial role in network collective dynamics. However, their impact on ISR lacks systematic exploration. This study constructs an electrically coupled network of bistable FitzHugh-Nagumo (FHN) neurons and investigates how higher-order and pairwise interactions modulate ISR. It is found that the emergence of the ISR effect depends on the synergistic modulation between noise, pairwise interactions, and higher-order interactions. Pairwise and higher-order interactions can weaken and even completely eliminate ISR. Furthermore, it is found that higher-order interactions exert a stronger suppression effect on ISR than pairwise interactions. Moreover, the initial value distribution of network neurons is closely related to the generation of ISR. Finally, the robustness of the findings with respect to network topology is verified in both small-world and scale-free networks. This paper extends the study of ISR to more realistic neuronal systems, offering a theoretical perspective for understanding ISR in complex neural systems.

Keywords: Inverse stochastic resonance; Higher-order interactions; Pairwise interactions; Bistable FitzHugh-Nagumo neurons (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:209:y:2026:i:p1:s0960077926006296

DOI: 10.1016/j.chaos.2026.118488

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