Neural information processing and time-series prediction with only two dynamical memristors

TL;DR

This paper introduces a novel approach to neural information processing using dynamical memristors, enabling complex temporal tasks like spike detection and time-series prediction without relying solely on resistance state programmability.

Contribution

It demonstrates the use of simple dynamical memristor circuits for complex temporal processing, including autonomous spike detection and high-accuracy time-series prediction.

Findings

Memristor circuits can detect neural spike patterns in noisy environments.

A two-memristor system can learn and predict dynamical system behavior.

Dynamical properties of memristors are effectively exploited for neural computation.

Abstract

Memristive devices are commonly benchmarked by the multi-level programmability of their resistance states. Neural networks utilizing memristor crossbar arrays as synaptic layers largely rely on this feature. However, the dynamical properties of memristors, such as the adaptive response times arising from the exponential voltage dependence of the resistive switching speed remain largely unexploited. Here, we propose an information processing scheme which fundamentally relies on the latter. We realize simple dynamical memristor circuits capable of complex temporal information processing tasks. We demonstrate an artificial neural circuit with one nonvolatile and one volatile memristor which can detect a neural spike pattern in a very noisy environment, fire a single voltage pulse upon successful detection and reset itself in an entirely autonomous manner. Furthermore, we implement a circuit with only two nonvolatile memristors which can learn the operation of an external dynamical system and perform the corresponding time-series prediction with high accuracy.