Memristor-based neural networks

TLDR

The synapse is essential to biological neural networks, yet no simple electronic equivalent existed, hindering hardware replication until recent advances in memristive devices revived interest. The study aims to explain biological synaptic mechanisms and establish minimal requirements for an artificial neural network using memristors. Memristive systems, whose resistance depends on past states, can emulate synaptic connections, and the authors review both simple circuit implementations and more elaborate designs that mimic biological processes.

Abstract

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them.