Adaptive SRM neuron based on NbOx memristive device for neuromorphic computing

Abstract

The spike-response model (SRM) describes the adaptive behaviors of a biological neuron in response to repeated or prolonged stimulation, so that SRM neurons can avoid information overload and support neural networks for competitive learning. In this work, an artificial SRM neuron with the leaky integrate-and-fire (LIF) functions and the adaptive threshold is firstly implemented by the volatile memristive device of Pt/NbOx/TiN. By modulating the volatile speed of the device, the threshold of the SRM neuron is adjusted to achieve the adaptive behaviors, such as the refractory period and the lateral inhibition. To demonstrate the function of the SRM neuron, a spiking neural network (SNN) is constructed with the SRM neurons and trained by the unsupervised learning rule, which successfully classifies letters with noises, while a similar SNN with LIF neurons fails. This work demonstrates that the SRM neuron not only emulates the adaptive behaviors of a biological neuron, but also enriches the functionality and unleashes the computational power of SNNs.