Iontronics of nanofluidic conical pores: learning phenomena using voltage pulses

Abstract

Nanofluidic memristors exhibit conductance memory effects that can be used in neuromorphic computing, an emerging technology reminiscent of the brain synapses that change their connection strength in response to electrical signals. We describe different options allowing the modulation of the conductance by programming series of rectangular voltage pulses of different characteristics, amplitude, duration, and frequency. The resulting history-dependent conductance allows short-term memory states and learning procedures through potentiation (connection strengthening) and depression (connection weakening) effects. In addition, electrochemical networks of memristors provide operational procedures to implement both logical responses and reservoir computing algorithms using different series of voltage pulses as inputs. As a proof of concept, the supervised learning of a memristive array allows playing a tic-tac-toe game.