Power-law statistics of synchronous transition in inhibitory neuronal networks  

作　　者：Lei Tao Sheng-Jun Wang 陶蕾;王圣军(School of Physics and Information Technology,Shaanxi Normal University,Xi’an 710119,China)

机构地区：[1]School of Physics and Information Technology,Shaanxi Normal University,Xi’an 710119,China

出　　处：《Chinese Physics B》2022年第8期310-316,共7页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Grant No. 11675096);the Fund for the Academic Leaders and Academic Backbones, Shaanxi Normal University, China (Grant No. 16QNGG007)。

摘　　要：We investigate the relationship between the synchronous transition and the power law behavior in spiking networks which are composed of inhibitory neurons and balanced by dc current. In the region of the synchronous transition, the avalanche size and duration distribution obey a power law distribution. We demonstrate the robustness of the power law for event sizes at different parameters and multiple time scales. Importantly, the exponent of the event size and duration distribution can satisfy the critical scaling relation. By changing the network structure parameters in the parameter region of transition, quasicriticality is observed, that is, critical exponents depart away from the criticality while still hold approximately to a dynamical scaling relation. The results suggest that power law statistics can emerge in networks composed of inhibitory neurons when the networks are balanced by external driving signal.

关 键 词：POWER-LAW INHIBITORY SYNCHRONIZATION neuronal networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]