Combining a volatile and nonvolatile memristor in artificial synapse to improve learning in Spiking Neural Networks

Abstract

With the end of Moore's law in sight, we need new computing architectures to satisfy the increasing demands of big data processing. Neuromorphic architectures are good candidates to low energy computing for recognition and classification tasks. We propose an event-based spiking neural network architecture based on artificial synapses. We introduce a novel synapse box that is able to forget and remember by inspiration from biological synapses. Two different volatile and nonvolatile memristor devices are combined in the synapse box. To evaluate the effectiveness of our proposal, we use system-level simulation in our Neural Network Scalable Spiking Simulator (N2S3) using the MNIST handwritten digit recognition dataset. The first results show better performance of our novel synapse than the traditional nonvolatile artificial synapses.