Fully Unsupervised Spike-Rate-Dependent Plasticity Learning With Oxide- Based Memory Devices

Artificial synapses are fundamental building blocks for neuromorphic computing hardware. In this work, we demonstrate fully unsupervised neuromorphic learning using analog weight change properties of bilayer nonfilamentry oxide-based resistive memory (OxRAM) devices as synaptic elements. Essential functions of a biological synapse, such as potentiation, depression, and spike-rate-dependent plasticity (SRDP), are experimentally demonstrated. Furthermore, we show the tune-ability of SRDP with preneuron/postneuron spike-train parameters (frequency and amplitude). Through simulations, we show a two-layer fully connected neuromorphic network powered by SRDP and OxRAM synapses. Our network achieves state-of-the-art best classification accuracy results compared to the literature (for SRDP-based purely unsupervised spiking neural networks (SNNs), without the use of any convolutional layer) on full Modified National Institute of Standards and Technology (MNIST) dataset: 92.07% (training) and 90.76% (test). Detailed impact of device properties, such as variability, retention, and endurance, is analyzed. The impact of introducing sparsity during training and weight pruning during inference was also investigated.