Real-Time Correlation Detection via Online Learning of a Spiking Neural Network with a Conductive-Bridge Neuron

Abstract

The neuronal density of complementary metal-oxide-semiconductor field-effect transistor-based neurons is limited because of the use of capacitors. Therefore, a novel neuron is fabricated using a conductive-bridge-neuron device, current-mirror-type sense amplifier, latch, micro-controller-unit, and digital-analog-converters. This neuron exhibits a typical integrate-and-fire function; in particular, the generation frequency of the fire spikes at the neuron exponentially increases with the input-voltage-spike amplitude. Using the proposed designed neuron in combination with an input spike generation and spike-timing-dependent-plasticity algorithm, a real-time correlation detection based on online learning is realized. With the increase in the number of learning iterations, the weight of synapses for 100 correlated input neurons gradually increase, whereas that for 900 uncorrelated input neurons steadily reduce. In addition, after 700 learning iterations, the output neuron is almost synchronized with the 100 correlated input neurons, thereby achieving correlation detection for cognitive functions in neuromorphic architectures and demonstrating the possibility of development of a neuromorphic chip based on the conductive-bridge neurons and synapses.