Unsupervised learning in hexagonal boron nitride memristor-based spiking neural networks.

Resistive Random Access Memory (RRAM) is an emerging non-volatile memory (NVM) technology that can be used in neuromorphic computing hardware to overcome the limitations of traditional von Neumann architectures by merging of processing and memory units. Two-dimensional (2D) materials with non-volatile behavior can be used as the switching layer of RRAMs, exhibiting superior behavior compared to conventional oxide-based devices. In this study, we investigate the electrical performance of 2D hexagonal Boron Nitride (h-BN) memristors towards their implementation in spiking neural networks (SNN). Based on experimental behavior of the h-BN memristors as artificial synapses, we simulate the implementation of unsupervised learning in spiking neural network (SNN) for image classification on the Modified National Institute of Standards and Technology (MNIST) dataset. Additonally, we propose a simple Spike-Timing-Dependent-Plasticity (STDP)-based dropout technique to enhance the recognition rate in h-BN memristor-based SNN. Our results demonstrate the viability of using 2D-material-based memristors as artificial synapses to perform unsupervised learning in SNN using hardware-friendly methods for online learning.&#xD.