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Title
Japanese:Noise-tuned bursting in a Hedgehog burster 
English:Noise-tuned bursting in a Hedgehog burster 
Author
Japanese: Jinjie Zhu, 中尾裕也.  
English: Jinjie Zhu, Hiroya Nakao.  
Language English 
Journal/Book name
Japanese:Frontiers in Computational Neuroscience 
English:Frontiers in Computational Neuroscience 
Volume, Number, Page Vol. 16       
Published date July 28, 2022 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL http://dx.doi.org/10.3389/fncom.2022.970643
 
DOI https://doi.org/10.3389/fncom.2022.970643
Abstract <jats:p>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 <jats:italic>via</jats:italic> 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.</jats:p>

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