Reservoir Computing with Neuromemristive Nanowire Networks

Abstract

We present simulations based on a model of self- assembled nanowire networks with memristive junctions and neural network-like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto- generation and forecasting the Mackey-Glass chaotic time series.