Evolution of Plastic Learning in Spiking Networks via Memristive Connections

TL;DR

This paper introduces a neuroevolutionary system using memristors as plastic connections in spiking neural networks, demonstrating improved learning efficiency and performance in robotic navigation tasks through heterogeneous memristive networks.

Contribution

It presents a novel neuroevolutionary approach incorporating memristive plasticity, enabling emergent network complexity and superior performance over traditional fixed-weight connections.

Findings

Memristive connections reduce learning time in neural networks.

Heterogeneous memristor mixtures outperform homogeneous networks.

Networks achieve higher performance in robotic navigation tasks.

Abstract

This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of (i) linear resistors (ii) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks.