Memristive Logic Design of Multifunctional Spiking Neural Network with Unsupervised Learning

Abstract

We report a prospective approach to neural network modeling based on implementation of metal-oxide heterostructures with non-volatile memory behavior and multilevel resistive switching. These structures could be used as artificial synapses in neural networks, providing a modulation of synaptic strength in the range of seven orders of magnitude. Together with leaky integrated-and-fire neurons, it allows to organize a feed-forward spiking neural network with embedded spike-timing-dependent plasticity mechanism at the hardware level. The results of computer simulation demonstrate an ability of reconstructed networks based on electronic multilevel synapses to unsupervised learning and processing of asynchronous stream of spikes. Following the results of computer simulation, the biologically inspired circuit design for artificial neural network with memristive synapses was developed. The idea underlying the circuit design is based on the analogue approach and implies the hybrid CMOS-neurons/memristive synapses network, where operational amplifiers used as elements of current control through the memristive devices.