An Analog Implementation of FitzHugh-Nagumo Neuron Model for Spiking Neural Networks

Raunak Borwankar, Anurag Desai, Mohammad R. Haider, Reinhold Ludwig, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

A low power analog implementation of FitzHugh-Nagumo (FHN) neuron model is presented in this paper for large scale spiking neural network and neuromorphic algorithm realization. The FHN neuron model is designed using log-domain low pass filters and translinear multipliers to emulate voltage-like variable with cubic non-linearity and a recovery variable. Various spiking behaviors observed in biological neurons are demonstrated in simulation results. The neuron model was designed in 45 nm CMOS process which has 1.6 nW and 40 nW power consumption at rest and for a single spiking event respectively.
Original languageEnglish (US)
Title of host publication2018 16th IEEE International New Circuits and Systems Conference, NEWCAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages134-138
Number of pages5
ISBN (Print)9781538615133
DOIs
StatePublished - Dec 21 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

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