Biomimetic Spiking Neural Network Based on Monolayer 2-D Synapse With Short-Term Plasticity for Auditory Brainstem Processing

Abstract

In the sound localization of species, short-term depression (STD) plays an important role in maintaining interaural timing difference (ITD) sensitivity. In this article, a biomimetic spiking neural network (SNN) utilizing 2-D synaptic devices for mimicking biological sound localization is presented. A two-terminal monolayer device is used as the artificial synapse, whose temporal conductance change mimics the STD of a synapse. Alpha synaptic current and leaky integrate-and-fire (LIF) neuron models are used for realistic cortical operation. Lateral inhibition and superior olivary nucleus (SON) are adopted to increase the acuteness, to compensate for the interaural level difference (ILD)-induced disturbance, and to enlarge the sound intensity range. By combining solid-state STD synapses and bio-plausible cortical models with an ITD-based coincidence detection mechanism to mimic the auditory brainstem processing, our SNN achieved sound localization with a human-level resolution of 1°.