Hebbian Learning in Spiking Neural Networks With Nanocrystalline Silicon TFTs and Memristive Synapses

Abstract

Characteristics similar to biological neurons are demonstrated in SPICE simulations of spiking neuron circuits comprised of submicron nanocrystalline silicon (nc-Si) thin-film transistors (TFTs). Utilizing these neuron circuits and corresponding device models, the properties of a two-neuron network are explored. The synaptic connection consists of a single nc-Si TFT and a memristor whose conductance determines the synaptic weight. During correlated spiking of the pre- and postsynaptic neurons, the strength of the synaptic connection increases. Conversely, it is diminished when the spiking is uncorrelated. This synaptic plasticity and Hebbian learning are essential for performing useful computation and adaptation in large-scale artificial neural networks. The importance of the result is augmented by the fact that these properties are demonstrated using models based on measured data from devices with potential for 3-D integration into a nanoscale architecture with extremely high device density.