Power-law statistics of synchronous transition in inhibitory neuronal networks |
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| Affiliation: | 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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| Keywords: | power-law inhibitory synchronization neuronal networks |
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