A Simple Memristive Circuit for Pattern Classification Based on Reservoir Computing

Abstract

Reservoir computing is a machine learning framework suited for pattern recognition of temporal signals. A reservoir computing system normally consists of a dynamic reservoir and a static readout. Memristive systems and devices with intrinsic nonlinearity are potentially favorable for constructing a reservoir hardware because the role of the reservoir is to nonlinearly map input signals into a higher-dimensional feature space. In this study, we propose a minimal configuration of memristive circuit reservoirs for solving temporal pattern classification problems. We numerically demonstrate how the classification performance of the proposed reservoir computing systems with only two memristive devices depends on the system conditions in waveform and electrocardiogram classification tasks. The signal processing methods used for performance improvement provide useful insights into effective and efficient implementation of memristive reservoir computing hardware.