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.

Original languageEnglish
Pages (from-to)477-488
Number of pages12
JournalAnalog Integrated Circuits and Signal Processing
Issue number3
Publication statusPublished - 2019

    Research areas

  • Neuromorphic design, Receptor, Synapse, Synaptic plasticity, Synchrony detection

ID: 50089612