Enhancing memory capacity of reservoir computing via external structures: Delay, Passthrough, and Parallel Connections

Abstract

We propose novel methods for enhancing the memory capacity of reservoir computing (RC) solely by modifying the network configuration without changing the RC layer. Delay, Passthrough, and Parallel methods are introduced and applied to the echo state network (ESN) and chaotic Boltzmann machine (CBM-)RC. Evaluations using the NARMA task and information processing capacity (IPC) showed that these methods significantly improve memory capacity while enabling control over the memory-nonlinearity trade-off. The Delay-Passthrough combination yielded the best performance across models, particularly benefiting CBM-RC, which is advantageous for analog VLSI implementations where internal modification is constrained.