Abstract
The simulation of large spiking neural networks (SNN) is still a very time consuming task. Therefore most simulations are limited to rather unrealistic small or medium sized networks (typically hundreds of neurons). In this paper, some methods for the fast simulation of large SNN are discussed. Our results equally amongst others show that event based simulation is an efficient way of simulating SNN, although not all neuron models are suited for an event based approach. We compare some models and discuss several techniques for accelerating the simulation of more complex models. Finally we present an algorithm that is able to handle multi-synapse models efficiently.
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References
Brette, R.: Exact simulation of integrate-and-fire models with synaptic conductances (2005) (Submitted)
Carrillo, R., Ros, E., Ortigosa, E., Barbour, B., Agis, R.: Lookup table powered neural event-driven simulator. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 168–175. Springer, Heidelberg (2005)
D’Haene, M.: Parallelle event-gebaseerde simulatietechnieken en hun toepassing binnen gepulste neurale netwerken. Technical report, Universiteit Gent (2005)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)
Grassmann, C., Anlauf, J.: Distributed, event driven simulation of spiking neural networks. In: Proceedings of the International ICSC /IFAC Symposium on Neural Computation, pp. 100–105 (1998)
Graves, A., Eck, D., Beringer, N., Schmidhuber, J.: Biologically plausible speech recognition with LSTM neural nets. In: Proceedings of Bio-ADIT, pp. 127–136 (2004)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. J. Physiol (London) 117, 500–544 (1952)
Lobb, C., Chao, Z., Fujimoto, R., Potter, S.: Parallel event-driven neural network simulation using the Hodgkin-Huxley neuron model. In: Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation (2005)
Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Computation 8(1), 1–40 (1996)
Maass, W.: Computation with spiking neurons. In: The Handbook of Brain Theory and Neural Networks. The MIT Press, Cambridge (2001)
Makino, T.: A discrete-event neural network simulator for general neural models. Neural Computing & Applications 11, 210–223 (2003)
Rochel, O., Martinez, D.: An event-driven framework for the simulation of networks of spiking neurons. In: Proceedings of the 11th European Symposium on Artifical Neural Networks, ESANN 2003, pp. 295–300 (2003)
Schrauwen, B.: Embedded spiking neural networks. In: Doctoraatssymposium Faculteit Toegepaste Wetenschappen, pages on CD–ROM. Universiteit Gent, Gent (December 2002)
Schrauwen, B., D‘Haene, M.: Compact digital hardware implementations of spiking neural networks. In: Van Campenhout, J. (ed.) Sixth FirW PhD Symposium, page on CD, 1 (2005)
Schrauwen, B., Van Campenhout, J.: Parallel hardware implementation of a broad class of spiking neurons using serial arithmetic. Proceedings of ESANN (2006) (To be published)
Softky, W.R.: Simple codes versus efficient codes. Current opinion in neurobiology 5, 239–247 (1995)
Verstraeten, D., Schrauwen, B., Stroobandt, D.: Reservoir-based techniques for speech recognition, Accepted for publication at WCCI 2006 (2006)
Verstraeten, D., Schrauwen, B., Stroobandt, D., Van Campenhout, J.: Isolated word recognition with the liquid state machine: a case study. Information Processing Letters 95(6), 521–528 (2005)
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D’Haene, M., Schrauwen, B., Stroobandt, D. (2006). Accelerating Event Based Simulation for Multi-synapse Spiking Neural Networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_79
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DOI: https://doi.org/10.1007/11840817_79
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