Edge-Of-Chaos Learning Achieved by Ion-Electron Coupled Dynamics in an Ion-Gating Reservoir

TL;DR

This paper introduces a novel ion-gating reservoir using Li+-electrolyte with ion-electron dynamics, achieving high computational performance and operating at the edge of chaos, suitable for advanced neural network devices.

Contribution

Development of a Li+-electrolyte based ion-gating reservoir with ion-electron coupled dynamics, demonstrating superior performance in physical reservoir computing.

Findings

Achieved a NMSE of 0.023 on NARMA2 task, the best reported for physical reservoirs.

Demonstrated operation at the edge of chaos with a Lyapunov exponent of 0.0083.

Enabled high-performance, integrated neural network devices.

Abstract

Physical reservoir computing has recently been attracting attention for its ability to significantly reduce the computational resources required to process time-series data. However, the physical reservoirs that have been reported to date have had insufficient expression power, and most of them have a large volume, which makes their practical application difficult. Herein we describe the development of a Li+-electrolyte based ion-gating reservoir (IGR), with ion-electron coupled dynamics, for use in high performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which responses were stored as transient charge density patterns in an electric double layer, at the Li+-electrolyte/diamond interface. Performance, which was tested using a nonlinear autoregressive moving-average (NARMA) task, was found to be excellent, with a NMSE of 0.023 for NARMA2, which is the highest for any physical reservoir reported to date. The maximum Lyapunov exponent of the IGR was 0.0083: the edge of chaos state enabling the best computational capacity. The IGR described herein opens the way for high-performance and integrated neural network devices.