Neural Information Processing and Time‐Series Prediction with Only Two Dynamical Memristors

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 tailorable response times arising from the exponential voltage dependence of the resistive switching speed remain largely unexploited. Here, an information processing scheme which fundamentally relies on the latter is proposed. Simple dynamical memristor circuits capable of solving complex temporal information processing tasks are realized. A scheme is presented in which a single non‐volatile meristor and a series resistor can perform temporal pattern recognition tasks, such as the discrimination of sub‐threshold and super‐threshold voltage pulses, or the identification of neural spikes buried in high noise. By coupling to an oscillator circuit of a volatile Mott memristor, a complete neural circuit is realized that fires an output pulse upon signal detection and resets itself in a fully autonomous manner. Furthermore, a time series prediction circuit is implemented using a dynamic layer of only two memristors and a readout layer based on the linear combination of their output signals. This scheme can learn the operation of an external dynamical system and predict its output with high accuracy.