Evolving spiking networks with variable memristors

Abstract

This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to be evolved for each network. Additionally, each memristor has its own conductance profile, which alters the neuromodulatory behaviour of the memristor and may be altered during the application of the GA. We demonstrate that this approach allows the evolutionary process to discover beneficial memristive behaviours at specific points in the networks. We evaluate our approach against two phenomenological real-world memristive implementations, a theoretical "linear memristor", and a system containing standard connections only. Performance is evaluated on a simulated robotic navigation task.