Abstract
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.
Highlights
The cerebellum is an extensively studied brain area heavily involved in motor learning and coordination (Eccles et al, 1967; Ito, 2011)
It can be viewed as an area of extremes, containing both the most numerous neural cell type in the human brain— granule cell number estimated at 5 × 1010, ∼2.5 times more numerous than the neural cells in the neocortex (Andersen et al, 2003; Shepherd, 2004; Walløe et al, 2014)—and the cell type receiving the highest number of afferent synapses—Purkinje cells can have a synaptic fan-in estimated on the order of 100,000 parallel fibers (Napper and Harvey, 1988; Tyrrell and Willshaw, 1992; Hoxha et al, 2016)
The results presented here consist of two parts: neural spiking activity validation and analysis of communication requirements. The former involves small- and large-scale experiments simulated on SpiNNaker and NEST, with the activity produced by NEST treated as a baseline for comparison
Summary
The cerebellum is an extensively studied brain area heavily involved in motor learning and coordination (Eccles et al, 1967; Ito, 2011) It can be viewed as an area of extremes, containing both the most numerous neural cell type in the human brain— granule cell number estimated at 5 × 1010, ∼2.5 times more numerous than the neural cells in the neocortex (Andersen et al, 2003; Shepherd, 2004; Walløe et al, 2014)—and the cell type receiving the highest number of afferent synapses—Purkinje cells can have a synaptic fan-in estimated on the order of 100,000 parallel fibers (Napper and Harvey, 1988; Tyrrell and Willshaw, 1992; Hoxha et al, 2016). The technologies required to support such a wide range of scales and model vary in scale and specialization and can be categorized into conventional and neuromorphic computing solutions
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