Optimizing Memory in Reservoir Computers

TL;DR

This paper explores methods to control and optimize the fading memory in reservoir computers by adjusting network parameters, which is crucial for enhancing their computational performance.

Contribution

It introduces techniques to vary the fading memory length in reservoir computers, aiding in optimal tuning for specific computational tasks.

Findings

Memory length can be tuned for better performance

Overly long or short memory degrades accuracy

Methods enable tailored reservoir computer design

Abstract

A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between nodes, the reservoir computer has memory. If the reservoir computer is to respond to an input signal in a consistent way (a necessary condition for computation), the memory must be fading; that is, the influence of the initial conditions fades over time. How long this memory lasts is important for determining how well the reservoir computer can solve a particular problem. In this paper I describe ways to vary the length of the fading memory in reservoir computers. Tuning the memory can be important to achieve optimal results in some problems; too much or too little memory degrades the accuracy of the computation.