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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 |
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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.
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Received: 26 November 2021
Revised: 24 February 2022
Accepted manuscript online: 10 March 2022
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PACS:
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05.45.Xt
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(Synchronization; coupled oscillators)
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05.70.Jk
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(Critical point phenomena)
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87.85.dq
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(Neural networks)
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87.19.lm
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(Synchronization in the nervous system)
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| Fund: 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). |
Corresponding Authors:
Sheng-Jun Wang
E-mail: wangshjun@snnu.edu.cn
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Cite this article:
Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军) Power-law statistics of synchronous transition in inhibitory neuronal networks 2022 Chin. Phys. B 31 080505
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