Scaling behaviors and self-organized criticality of two-dimensional small-world neural networks

HL Zeng, CP Zhu, SX Wang, YD Guo, ZM Gu… - Physica A: Statistical …, 2020 - Elsevier
HL Zeng, CP Zhu, SX Wang, YD Guo, ZM Gu, CK Hu
Physica A: Statistical Mechanics and its Applications, 2020Elsevier
It is widely believed that the brains of human beings work at or near the state of self-
organized criticality (SOC). In the present work, we investigate two-dimensional small-world
neural networks (2D SWNN) with Bak–Sneppen (BS)-type neurons as their nodes. By taking
threshold firing and refractory period as the key features of neurons in the simulations, a few
power laws are obtained for suitable range of parameters. The SOC characterized by the
power-law distribution of avalanche sizes as well as 1∕ f noise emerges in the present …
It is widely believed that the brains of human beings work at or near the state of self-organized criticality (SOC). In the present work, we investigate two-dimensional small-world neural networks (2D SWNN) with Bak–Sneppen (BS)-type neurons as their nodes. By taking threshold firing and refractory period as the key features of neurons in the simulations, a few power laws are obtained for suitable range of parameters. The SOC characterized by the power-law distribution of avalanche sizes as well as 1∕ f noise emerges in the present model. Moreover, a set of scaling relations are found to exhibit criticality. The exponent for the power spectrum of all return time is α= 0. 71, which is comparable with what were found in medical experiments.
Elsevier
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