Sen-Bhattacharya, B., James, S. orcid.org/0000-0003-0208-0588, Rhodes, O. et al. (5 more authors) (2018) Building a Spiking Neural Network Model of the Basal Ganglia on SpiNNaker. IEEE Transactions on Cognitive and Developmental Systems, 10 (3). pp. 823-836. ISSN 2379-8920
Abstract
We present a biologically-inspired and scalable model of the Basal Ganglia (BG) simulated on the SpiNNaker machine, a biologically-inspired low-power hardware platform allowing parallel, asynchronous computing. Our BG model consists of six cell populations, where the neuro-computational unit is a conductance-based Izhikevich spiking neuron; the number of neurons in each population is proportional to that reported in anatomical literature. This model is treated as a single-channel of action-selection in the BG, and is scaled-up to three channels with lateral cross-channel connections. When tested with two competing inputs, this three-channel model demonstrates action-selection behaviour. The SpiNNaker-based model is mapped exactly on to SpineML running on a conventional computer; both model responses show functional and qualitative similarity, thus validating the usability of SpiNNaker for simulating biologically-plausible networks. Furthermore, the SpiNNaker-based model simulates in real time for time-steps 1 ms; power dissipated during model execution is & #x2248;1.8 W.
Metadata
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Copyright, Publisher and Additional Information: | © 2018 The Authors. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. | ||||||
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Institution: | The University of Sheffield | ||||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
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Depositing User: | Symplectic Sheffield | ||||||
Date Deposited: | 03 Apr 2018 13:14 | ||||||
Last Modified: | 16 Nov 2020 11:01 | ||||||
Status: | Published | ||||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
Refereed: | Yes | ||||||
Identification Number: | https://doi.org/10.1109/TCDS.2018.2797426 |