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

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

doi:10.1109/NEWCAS.2018.8585554

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 proceeding › Conference contribution

6 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 language	English (US)
Title of host publication	2018 16th IEEE International New Circuits and Systems Conference, NEWCAS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	134-138
Number of pages	5
ISBN (Print)	9781538615133
DOIs	https://doi.org/10.1109/NEWCAS.2018.8585554
State	Published - Dec 21 2018
Externally published	Yes

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10.1109/NEWCAS.2018.8585554

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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.",

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An Analog Implementation of FitzHugh-Nagumo Neuron Model for Spiking Neural Networks. / Borwankar, Raunak; Desai, Anurag; Haider, Mohammad R. et al.
2018 16th IEEE International New Circuits and Systems Conference, NEWCAS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 134-138.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Desai, Anurag

AU - Haider, Mohammad R.

AU - Ludwig, Reinhold

AU - Massoud, Yehia

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PY - 2018/12/21

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AB - 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.

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BT - 2018 16th IEEE International New Circuits and Systems Conference, NEWCAS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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

Abstract

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