Performance Study by Changing the Internal Structure of Hysteresis Reservoir Computing

In the realm of reservoir computing, a well-designed internal structure of the reservoir layer can bestow enhanced memory capacity and greater expressive prowess. However, a noteworthy trade-off exists between memory capacity and expressiveness. Consequently, even with optimal internal configurations, achieving simultaneous improvements in both aspects remains a challenging endeavor. In this study, we empirically investigate the potential performance enhancements achievable through hysteresis reservoir computing. Specifically, this approach incorporates hysteresis neurons within the reservoir layer of traditional reservoir computing.