Exploring reservoir computing: Implementation via double stochastic nanowire networks

Abstract

Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture (DS-nanowire network) for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics, and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.