Power-law statistics of synchronous transition in inhibitory neuronal networks

doi:10.1088/1674-1056/ac5c31

中国物理B ›› 2022, Vol. 31 ›› Issue (8): 80505-080505.doi: 10.1088/1674-1056/ac5c31

Power-law statistics of synchronous transition in inhibitory neuronal networks

Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军)^†

School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China

收稿日期:2021-11-26 修回日期:2022-02-24 接受日期:2022-03-10 出版日期:2022-07-18 发布日期:2022-07-18
通讯作者: Sheng-Jun Wang E-mail:wangshjun@snnu.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 11675096) and the Fund for the Academic Leaders and Academic Backbones, Shaanxi Normal University, China (Grant No. 16QNGG007).

Power-law statistics of synchronous transition in inhibitory neuronal networks

Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军)^†

School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China

Received:2021-11-26 Revised:2022-02-24 Accepted:2022-03-10 Online:2022-07-18 Published:2022-07-18
Contact: Sheng-Jun Wang E-mail:wangshjun@snnu.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11675096) and the Fund for the Academic Leaders and Academic Backbones, Shaanxi Normal University, China (Grant No. 16QNGG007).

摘要/Abstract

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

Abstract: 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.

Key words: power-law, inhibitory, synchronization, neuronal networks

中图分类号: (Synchronization; coupled oscillators)

05.45.Xt

05.70.Jk (Critical point phenomena) 87.85.dq (Neural networks) 87.19.lm (Synchronization in the nervous system)

Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军). Power-law statistics of synchronous transition in inhibitory neuronal networks[J]. 中国物理B, 2022, 31(8): 80505-080505.

Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军). Power-law statistics of synchronous transition in inhibitory neuronal networks[J]. Chin. Phys. B, 2022, 31(8): 80505-080505.

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Power-law statistics of synchronous transition in inhibitory neuronal networks

Power-law statistics of synchronous transition in inhibitory neuronal networks

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