Skewed and Long-Tailed Distributions of Spiking Activity in Coupled Network Modules with Log-Normal Synaptic Weight Distribution

Recent studies with neuroimaging modalities have been elucidating a structure of a whole network of the brain and its functional activity. The characteristics of various functional neural activities and network structures exhibit skewed and long-tailed distributions. However, it remains unclear how heavy-tailed structural distribution affects functional distribution. In this study, we constructed spiking neural networks composed of two modules with excitatory post-synaptic potential (EPSP) following log-normal distribution. Through the evaluation of multi-scale entropy analysis and its surrogate data analysis, we reveal that the long-tailed synaptic weight distribution enhances the complexity of spiking activity at large temporal scales and emerges non-linear dynamics. Furthermore, we compared distribution of residence time in each spiking pattern between cases with/without large EPSPs. The results show that strong synapses are crucial in the heavy-tailed distribution of residence time.

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