Energy-Efficient Neuromorphic Receptors for Wide-Range Temporal Patterns of Post-Synaptic Responses

Abstract

In a neuromorphic integrated circuit synaptic dynamics are of great importance to capture accurate neural behaviors. In this paper, we propose a current-based synapse design mediated with multiple receptor types, namely AMPA, NMDA and GABAa, and a weight-dependent learning algorithm. Due to various biological conducting mechanisms, the receptors demonstrate different kinetics in response to stimulus. The designed circuit offers distinctive features of receptors as well as the joint synaptic function. An increased computation ability is verified through synchrony detection in a two-layer recurrent network of synapse clusters. The design implemented in TSMC 65 nm CMOS technology consumes 1.92, 3.36, 1.11 and 35.22 pJ per spike event of energy for AMPA, NMDA, GABAa receptors and the advanced learning circuit, respectively.