Inhibitory effect induced by fractional Gaussian noise in neuronal system

被引:0
|
作者
Li, Zhi-Kun [1 ]
Li, Dong-Xi [2 ]
机构
[1] Taiyuan Univ Technol, Coll Math, Taiyuan 030024, Peoples R China
[2] Taiyuan Univ Technol, Coll Data Sci, Taiyuan 030024, Peoples R China
基金
中国国家自然科学基金;
关键词
inhibitory effect; inverse stochastic resonance; fractional Gaussian noise; neuronal system; BROWNIAN-MOTION; STOCHASTIC CALCULUS; SPIKING; BEHAVIOR;
D O I
10.1088/1674-1056/ac6332
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We discover a phenomenon of inhibition effect induced by fractional Gaussian noise in a neuronal system. Firstly, essential properties of fractional Brownian motion (fBm) and generation of fractional Gaussian noise (fGn) are presented, and representative sample paths of fBm and corresponding spectral density of fGn are discussed at different Hurst indexes. Next, we consider the effect of fGn on neuronal firing, and observe that neuronal firing decreases first and then increases with increasing noise intensity and Hurst index of fGn by studying the time series evolution. To further quantify the inhibitory effect of fGn, by introducing the average discharge rate, we investigate the effects of noise and external current on neuronal firing, and find the occurrence of inhibitory effect about noise intensity and Hurst index of fGn at a certain level of current. Moreover, the inhibition effect is not easy to occur when the noise intensity and Hurst index are too large or too small. In view of opposite action mechanism compared with stochastic resonance, this suppression phenomenon is called inverse stochastic resonance (ISR). Finally, the inhibitory effect induced by fGn is further verified based on the inter-spike intervals (ISIs) in the neuronal system. Our work lays a solid foundation for future study of non-Gaussian-type noise on neuronal systems.
引用
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页数:8
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