Synaptic dynamics is of great importance in realizing biophysically accurate neural behaviors and efficient synaptic learning in neuromorphic integrated circuits. In this paper, we propose a current-based synapse structure with multi-compartment receptors AMPA, NMDA and GABAa and a weight-dependent learning algorithm. The designed circuit offers distinctive dynamic features of receptors as well as a joint synaptic function. A cross-correlation methodology is applied to a two-layer RNN built by multi-compartment receptors to demonstrate the proposed synapse structure. An increased computation efficiency is verified through temporal synchrony detection among the neural layers in a noisy environment. 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 and the advanced learning circuit, respectively.

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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.@en