Functionality of neural dynamics induced by long-tailed synaptic distribution in reservoir computing

Abstract

In the cerebral cortex, excitatory postsynaptic potentials (EPSPs) exhibit a long-tailed distribution. Although EPSPs induce rich neural activity, their contributions to brain function remain unclear. Therefore, this study evaluated the effect of the dynamics induced by long-tailed synaptic weights by constructing a reservoir computing (RC) model and comparing the memory capacity and predictive accuracy for nonlinear time-series between RCs, with and without strong weights. The results revealed that strong weights significantly enhance the RC performance through gamma-band dynamic neural activity. This mechanism may support the cognitive processes in the actual brain network.