Efficient modelling of spiking neural networks on a scalable chip multiprocessor

@article{Jin2008EfficientMO,
  title={Efficient modelling of spiking neural networks on a scalable chip multiprocessor},
  author={Xin Jin and Stephen B. Furber and John V. Woods},
  journal={2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)},
  year={2008},
  pages={2812-2819}
}
  • Xin Jin, S. Furber, J. V. Woods
  • Published 1 June 2008
  • Computer Science
  • 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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… 
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