Excitatory/Inhibitory Balance Emerges as a Key Factor for RBN
  Performance, Overriding Attractor Dynamics
Emmanuel Calvet Jean Rouat Bertrand Reulet
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 attractor. We
then evaluate performance in two challenging tasks, memorization and
prediction, and find that a positive excitatory balance produces a critical
point with higher memory performance. In comparison, a negative inhibitory
balance delivers another critical point with better prediction performance.
Interestingly, we show that the intrinsic attractor dynamics have little
influence on performance in either case.