Efficient modelling of spiking neural networks on a scalable chip multiprocessor

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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. ©2008 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
Number of pages7
ISBN (Print)9781424418213
Publication statusPublished - 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong
Duration: 1 Jul 2008 → …


Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CityHong Kong
Period1/07/08 → …
Internet address


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