Robust neuromorphic coupled oscillators for adaptive pacemakers

Abstract Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electr...

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Autores principales:	Renate Krause, Joanne J. A. van Bavel, Chenxi Wu, Marc A. Vos, Alain Nogaret, Giacomo Indiveri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f2ddac2a2984833b167a0a32c2dc452
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spelling	oai:doaj.org-article:1f2ddac2a2984833b167a0a32c2dc4522021-12-02T19:12:28ZRobust neuromorphic coupled oscillators for adaptive pacemakers10.1038/s41598-021-97314-32045-2322https://doaj.org/article/1f2ddac2a2984833b167a0a32c2dc4522021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97314-3https://doaj.org/toc/2045-2322Abstract Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact, low-power, spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator’s frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings from dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers.Renate KrauseJoanne J. A. van BavelChenxi WuMarc A. VosAlain NogaretGiacomo IndiveriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Renate Krause Joanne J. A. van Bavel Chenxi Wu Marc A. Vos Alain Nogaret Giacomo Indiveri Robust neuromorphic coupled oscillators for adaptive pacemakers
description	Abstract Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact, low-power, spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator’s frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings from dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers.
format	article
author	Renate Krause Joanne J. A. van Bavel Chenxi Wu Marc A. Vos Alain Nogaret Giacomo Indiveri
author_facet	Renate Krause Joanne J. A. van Bavel Chenxi Wu Marc A. Vos Alain Nogaret Giacomo Indiveri
author_sort	Renate Krause
title	Robust neuromorphic coupled oscillators for adaptive pacemakers
title_short	Robust neuromorphic coupled oscillators for adaptive pacemakers
title_full	Robust neuromorphic coupled oscillators for adaptive pacemakers
title_fullStr	Robust neuromorphic coupled oscillators for adaptive pacemakers
title_full_unstemmed	Robust neuromorphic coupled oscillators for adaptive pacemakers
title_sort	robust neuromorphic coupled oscillators for adaptive pacemakers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f2ddac2a2984833b167a0a32c2dc452
work_keys_str_mv	AT renatekrause robustneuromorphiccoupledoscillatorsforadaptivepacemakers AT joannejavanbavel robustneuromorphiccoupledoscillatorsforadaptivepacemakers AT chenxiwu robustneuromorphiccoupledoscillatorsforadaptivepacemakers AT marcavos robustneuromorphiccoupledoscillatorsforadaptivepacemakers AT alainnogaret robustneuromorphiccoupledoscillatorsforadaptivepacemakers AT giacomoindiveri robustneuromorphiccoupledoscillatorsforadaptivepacemakers
_version_	1718377038803369984

Robust neuromorphic coupled oscillators for adaptive pacemakers

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