Robust neuromorphic coupled oscillators for adaptive pacemakers
| Autor: | Alain Nogaret, Marc A. Vos, Chenxi Wu, Joanne J.A. van Bavel, Giacomo Indiveri, Renate Krause |
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| Přispěvatelé: | University of Zurich, Krause, Renate |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer science Science Computer Science - Emerging Technologies adaptive pacemaker Article 03 medical and health sciences 0302 clinical medicine Robustness (computer science) Electronic engineering 030304 developmental biology Electronic circuit Block (data storage) 10194 Institute of Neuroinformatics Cardiac device therapy Spiking neural network 0303 health sciences 1000 Multidisciplinary Multidisciplinary Quantitative Biology::Neurons and Cognition neurotrophic coupled oscillator Electrical and electronic engineering Power (physics) Controllability Emerging Technologies (cs.ET) Neuromorphic engineering Biomedical engineering Medicine 570 Life sciences biology Electronic hardware 030217 neurology & neurosurgery |
| Zdroj: | Krause, R, van Bavel, J, Wu, C, Indiveri, G, Nogaret, A & Vos, M 2021, ' Robust neuromorphic coupled oscillators for adaptive pacemakers ', Scientific Reports, vol. 11, no. 18073 . https://doi.org/10.1038/s41598-021-97314-3 Scientific Reports, 11 (1) Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.3929/ethz-b-000505999 |
| Popis: | 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. Scientific Reports, 11 (1) ISSN:2045-2322 |
| Databáze: | OpenAIRE |
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