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Identifying type I excitability using dynamics of stochastic neural firing patterns
Authors:Bing Jia  Huaguang Gu
Institution:1.School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092 China ;2.China School of Life Sciences, Shaanxi Normal University, Xi’an, 710062 China
Abstract:The stochastic firing patterns are simulated near saddle-node bifurcation on an invariant cycle corresponding to type I excitability in stochastic Morris–Lecar model. In absence of external periodic signal, the stochastic firing manifests continuous distribution in ISI histogram (ISIH), whose amplitude at first increases sharply and then decreases exponentially. In presence of the external periodic signal, stochastic firing patterns appear as two cases of integer multiple firing with multiple discrete peaks in ISIH. One manifests perfect exponential decay in all peaks and the other imperfect exponential decay except a lower first peak. These stochastic firing patterns simulated with or without external periodic signal can be demonstrated in the experiments on rat hippocampal CA1 pyramidal neurons. The exponential decay laws in the multiple peaks are also acquired using probability analysis method. The perfect decay law is determined by the independent characteristic within the firing while the imperfect decay law is from the inhibitory effect. In addition, the stochastic firing patterns corresponding to type I excitability are compared to those of type II excitability. The results not only reveal the dynamics of stochastic firing patterns with or without external signal corresponding to type I excitability, but also provide practical indicators to availably identify type I excitability.
Keywords:Neural firing pattern  Excitability  Type I excitability  Stochastic process  Integer multiple firing  Saddle-node bifurcation on an invariant cycle
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