Scaling behaviors and self-organized criticality of two-dimensional small-world neural networks
Abstract
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.
- Publication:
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Physica A Statistical Mechanics and its Applications
- Pub Date:
- February 2020
- DOI:
- 10.1016/j.physa.2019.123191
- Bibcode:
- 2020PhyA..54023191Z
- Keywords:
-
- Two-dimensional small-world neuron network (2D SWNN);
- Bak-Sneppen (BS)-type neuron;
- Scaling behaviors;
- Self-organized criticality (SOC)