Excitatory/inhibitory balance emerges as a key factor for RBN performance, overriding attractor dynamics

ABSTRACT: Reservoir computing provides a time and cost-efficient alternative to traditional learning methods. Critical regimes, known as the “edge of chaos,” have been found to optimize computational performance in binary neural networks. However, little attention has been devoted to studying reservoir-to-reservoir variability when investigating the link between connectivity, dynamics, and performance. As physical reservoir computers become more prevalent, developing a systematic approach to network design is crucial. In this article, we examine Random Boolean Networks (RBNs) and demonstrate that specific distribution parameters can lead to diverse dynamics near critical points. We identify distinct dynamical attractors and quantify their statistics, revealing that most reservoirs possess a dominant

SUBMITTER: Calvet E 

PROVIDER: S-EPMC10445160 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature