Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices

TL;DR

This paper demonstrates that binary stochastic STDP learning can be achieved using chalcogenide-based memristive devices, advancing neuromorphic hardware with biologically inspired online learning capabilities.

Contribution

It provides an experimental demonstration of implementing binary stochastic STDP learning with memristors, a novel approach for neuromorphic systems.

Findings

Binary stochastic STDP achieved with memristors

Experimental validation of pulse-based learning

Potential for scalable neuromorphic hardware

Abstract

The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic systems. Large-scale neuromorphic hardware platforms are being developed with increasing number of neurons and synapses, having a critical bottleneck in the online learning capabilities. Spike-timing-dependent plasticity (STDP) is a widely used learning mechanism inspired by biology which updates the synaptic weight as a function of the temporal correlation between pre- and post-synaptic spikes. In this work, we demonstrate experimentally that binary stochastic STDP learning can be obtained from a memristor when the appropriate pulses are applied at both sides of the device.