Learning to Approximate Functions Using Nb-doped SrTiO$_3$ Memristors

TL;DR

This paper demonstrates that Nb-doped SrTiO$_3$ memristors can be used as synapses in neural networks to learn and approximate functions, showing robustness despite device noise and variability.

Contribution

It introduces a novel supervised learning algorithm for memristor-based neural networks and validates their potential as universal function approximators.

Findings

Memristor-based neural networks can learn functions despite device noise.

Discrete local updates enable robust learning in memristor synapses.

The proposed model is among the first to use memristors as universal function approximators.

Abstract

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilised Nb-doped SrTiO$_3$ memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalised conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise at each timestep. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.