Neuromorphic Architecture for Small-Scale Neocortical Network Emulation

Research output: Contribution to conferencePaperpeer-review


The paper presents a neuromorphic platform that can emulate a small-scale cortical network with diverse types of neurons and synapses found in cortical circuits. The platform provides configurable neurons, which behave similarly to the electrophysiological behaviours of different classes of pyramidal and interneurons, and configurable long- and short- term dynamic synapses that can provide inhibition, excitation, weight depressing and facilitating and spike-time dependent plasticity (STDP) dynamics. The prototype of the platform presented in this paper uses a single Cortical Neural Layer (CNL) integrated circuit (IC), which facilitates a network of 120 neurons and 7560 synapses. The number of CNL ICs used in the proposed architecture can be increased to enable larger neural network emulation. The network connectivity is configured using an off-chip Field Programmable Gate Array (FPGA) device. The parameters of the neural elements of the network can be configured using a computer-controlled bias voltages generator. To prove the concept in hardware, Winner-Take-All and Synfire chain networks have been implemented on the platform, and the results are presented.

Bibliographical metadata

Original languageEnglish
Number of pages8
Publication statusPublished - 9 Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Neuromorphic Cognitive Computing (IEEE SNCC) - Xiamen, China
Event duration: 6 Dec 20199 Dec 2019


Conference2019 IEEE Symposium Series on Computational Intelligence
Abbreviated titleIEEE SSCI 2019
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