Noise-tuned bursting in a Hedgehog burster

Jinjie Zhu; Hiroya Nakao

doi:10.3389/fncom.2022.970643

Noise-tuned bursting in a Hedgehog burster

Front Comput Neurosci. 2022 Jul 28:16:970643. doi: 10.3389/fncom.2022.970643. eCollection 2022.

Authors

Jinjie Zhu^{1

2}, Hiroya Nakao²

Affiliations

¹ School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China.
² Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan.

Abstract

Noise can shape the firing behaviors of neurons. Here, we show that noise acting on the fast variable of the Hedgehog burster can tune the spike counts of bursts via the self-induced stochastic resonance (SISR) phenomenon. Using the distance matching condition, the critical transition positions on the slow manifolds can be predicted and the stochastic periodic orbits for various noise strengths are obtained. The critical transition positions on the slow manifold with non-monotonic potential differences exhibit a staircase-like dependence on the noise strength, which is also revealed by the stepwise change in the period of the stochastic periodic orbit. The noise-tuned bursting is more coherent within each step while displaying mixed-mode oscillations near the boundaries between the steps. When noise is large enough, noise-induced trapping of the slow variable can be observed, where the number of coexisting traps increases with the noise strength. It is argued that the robustness of SISR underlies the generality of the results discovered in this paper.

Keywords: bursting; critical transition; fast-slow system; mixed-mode oscillation; noise-induced phenomenon; self-induced stochastic resonance.