Building a Spiking Neural Network Model of the Basal Ganglia on SpiNNaker

Sen-Bhattacharya, Basabdatta and James, Sebastian and Rhodes, Oliver and Sugiarto, Indar and Rowley, Andrew and Stokes, Alan Barry and Gurney, Kevin and Furber, Steve Bryan (2018) Building a Spiking Neural Network Model of the Basal Ganglia on SpiNNaker. [UNSPECIFIED]

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Abstract

We present a biologically inspired and scalable model of the basal ganglia (BG) simulated on the spiking neural network architecture (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 behavior. 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.

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