Supporting data for a study towards a bio-inspired real-time neuromorphic cerebellum

This work presents the first simulation of a large-scale, bio-inspired cerebellum model performed on neuromorphic hardware. A network containing 97k neurons and 4.2M synapses is simulated on the SpiNNaker neuromorphic system. The same model is also simulated using NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic, as a basis for validation. Individual cell and network-level spiking activity is validated against NEST and SpiNNaker is shown to produce results in agreement with NEST. The goal after validation is understanding how to accelerate the simulation speed of the network on the SpiNNaker system. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Understanding activity propagation through the network will be used to inform future development of a bespoke software solution for optimal execution of cerebellum models on the chosen many-core digital neuromorphic platform. The large ratio of granule cells to other cell types in the network results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. See also the associated GitHub repository.