Building a spiking neural network model of the Basal Ganglia on SpiNNaker

Basabdatta Sen Bhattacharya, Sebastian James, Oliver Rhodes, Indar Sugiarto, Andrew Rowley, Alan Stokes, Kevin Gurney, Stephen Furber

Research output: Contribution to journalArticlepeer-review


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 con-
sists 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 ≈ 1.8 W.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Cognitive and Developmental Systems
Publication statusPublished - 8 Mar 2018


  • asal Ganglia, SpiNNaker, biologically-inspired, SpineML, Spiking Neural Network, Izhikevich Neuron models


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