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Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum

This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation...

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Autores principales: Bogdan, Petruţ A., Marcinnò, Beatrice, Casellato, Claudia, Casali, Stefano, Rowley, Andrew G.D., Hopkins, Michael, Leporati, Francesco, D'Angelo, Egidio, Rhodes, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202688/
https://www.ncbi.nlm.nih.gov/pubmed/34135732
http://dx.doi.org/10.3389/fncel.2021.622870
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author Bogdan, Petruţ A.
Marcinnò, Beatrice
Casellato, Claudia
Casali, Stefano
Rowley, Andrew G.D.
Hopkins, Michael
Leporati, Francesco
D'Angelo, Egidio
Rhodes, Oliver
author_facet Bogdan, Petruţ A.
Marcinnò, Beatrice
Casellato, Claudia
Casali, Stefano
Rowley, Andrew G.D.
Hopkins, Michael
Leporati, Francesco
D'Angelo, Egidio
Rhodes, Oliver
author_sort Bogdan, Petruţ A.
collection PubMed
description This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms.
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spelling pubmed-82026882021-06-15 Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum Bogdan, Petruţ A. Marcinnò, Beatrice Casellato, Claudia Casali, Stefano Rowley, Andrew G.D. Hopkins, Michael Leporati, Francesco D'Angelo, Egidio Rhodes, Oliver Front Cell Neurosci Cellular Neuroscience This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms. Frontiers Media S.A. 2021-05-31 /pmc/articles/PMC8202688/ /pubmed/34135732 http://dx.doi.org/10.3389/fncel.2021.622870 Text en Copyright © 2021 Bogdan, Marcinnò, Casellato, Casali, Rowley, Hopkins, Leporati, D'Angelo and Rhodes. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular Neuroscience
Bogdan, Petruţ A.
Marcinnò, Beatrice
Casellato, Claudia
Casali, Stefano
Rowley, Andrew G.D.
Hopkins, Michael
Leporati, Francesco
D'Angelo, Egidio
Rhodes, Oliver
Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title_full Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title_fullStr Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title_full_unstemmed Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title_short Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum
title_sort towards a bio-inspired real-time neuromorphic cerebellum
topic Cellular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202688/
https://www.ncbi.nlm.nih.gov/pubmed/34135732
http://dx.doi.org/10.3389/fncel.2021.622870
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