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Physical reservoir computing has recently been attracting attention for itsability to significantly reduce the computational resources required to processtime-series data. However, the physical reservoirs that have been reported todate have had insufficient expression power, and most of them have a largevolume, which makes their practical application difficult. Herein we describethe development of a Li+-electrolyte based ion-gating reservoir (IGR), withion-electron coupled dynamics, for use in high performance physical reservoircomputing. A variety of synaptic responses were obtained in response to pastexperience, which responses were stored as transient charge density patterns inan 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, whichis the highest for any physical reservoir reported to date. The maximumLyapunov exponent of the IGR was 0.0083: the edge of chaos state enabling thebest computational capacity. The IGR described herein opens the way forhigh-performance and integrated neural network devices.

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