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Scaling behaviors and self-organized criticality of two-dimensional small-world neural networks
مقیاس بندی رفتارها و انتقاد خود سازمان یافته شبکه های عصبی دو بعدی دنیای کوچک-2020 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. Keywords: Two-dimensional small-world neuron | network (2D SWNN) | Bak–Sneppen (BS)-type neuron | Scaling behaviors | Self-organized criticality (SOC) |
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