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

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. Spiketiming- 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 postsynaptic 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.EU H2020 grant 824164 "HERMES"EU H2020 grant 871371 "Memscales"EU H2020 grant 871501 "NeurONN"EU H2020 grant PCI2019-111826-2 "APROVIS3D"EU H2020 grant 899559 "SpinAge"Ministry of Science and Innovation (Spain) PID2019-105556GB-C31Ministry of Science and Innovation ( Spain) PID2019-103876RB-I00 (CORDION)Ministry of Economy and Competitivity (Spain) / FEDER TEC2015- 63884-C2-1-P (COGNET)Junta de Andalucía (Spain) US-1260118 (Neuro-Radio)Universidad de Sevilla (Spain) VI PPI