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Efficient Modelling of Spiking Neural Networks on a Scalable Chip Multiprocessor

Xin Jin, S.B.Furber, J.V.Woods


We propose a system based on the Izhikevich model running on a scalable chip multiprocessor - SpiNNaker - for large-scale spiking neural network simulation. The design takes into account the requirements for processing, storage, and communication which are essential to the efficient modelling of spiking neural networks. To gain a speedup of the processing as well as saving storage space, the Izhikevich model is implemented in 16-bit fixed-point arithmetic. An approach based on using two scaling factors is developed, making the precision comparable to the original. With the two scaling factors scheme, all of the firing patterns by the original model can be reproduced with a much faster execution speed. To reduce the communication overhead, rather than sending synaptic weights on communicating, we only send out event packets to indicate the neuron firings while holding the synaptic weights in the memory of the post-synaptic neurons, which is so-called event-driven algorithm. The communication based on event packets can be handled efficiently by the multicast system supported by the SpiNNaker machine. We also describe a system level model for spiking neural network simulation based on the schemes above. The model has been functionally verified and experimental results are included. An analysis of the performance of the whole system is presented at the end of the paper.

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