A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors

Neural Networks

Volumn 22, Issue 5-6, 2009, Pages 791-800

  Nageswaran, Jayram Moorkanikara ^a   Dutt, Nikil ^a   Krichmar, Jeffrey L ^b   Nicolau, Alex ^a   Veidenbaum, Alexander V ^a  

^a Irvine   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

CUDA; Data parallelism; Graphics processor; Izhikevich spiking neuron; STDP

Indexed keywords

CUDA; DATA PARALLELISM; GRAPHICS PROCESSOR; IZHIKEVICH SPIKING NEURON; STDP;

ARCHITECTURAL DESIGN; COMPUTER ARCHITECTURE; COMPUTER NETWORKS; INTELLIGENT AGENTS; NEURAL NETWORKS; NEURONS;

SIMULATORS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN FUNCTION; BRAIN NERVE CELL; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER MODEL; COMPUTER SIMULATION; NERVE CELL PLASTICITY; NERVE FIBER; PRIORITY JOURNAL; SIMULATOR; SPIKE; VALIDATION PROCESS;

ACTION POTENTIALS; AXONS; COMPUTER SIMULATION; MEMBRANE POTENTIALS; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; SOFTWARE; SYNAPSES; TIME FACTORS;

EID: 68149182671     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.06.028     Document Type: Article

References (26)

1. Abeles M. Corticonics: Neural circuits of the cerebral cortex (1991), Cambridge University Press
2. Ananthanarayanan, R., & Modha, D. S. (2007). Anatomy of a cortical simulator. In Proceedings of the 2007 ACM/IEEE conference on supercomputing
3. Bernhard, F., & Keriven, R. (2006). Spiking neurons on GPUs. In International conference on computational science ICCS 2006 (pp. 236-243)
4. Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower J., et al. Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 23 3 (2007) 349-398
5. Davison A.P., Brderle D., Eppler J., Kremkow J., Muller E., Pecevski D., et al. PyNN: A common interface for neuronal network simulators. Frontiers in Neuroinformatics (2009)
6. Fatahalian K., and Houston M. A closer look at GPUs. Communications of ACM 51 10 (2008) 50-57
7. Izhikevich E.M. Simple model of spiking neurons. IEEE Transactions on Neural Networks 14 6 (2003) 1569-1572
8. Izhikevich E.M. Polychronization: Computation with spikes. Neural Computation 18 2 (2006) 245-282
9. Izhikevich E.M., and Edelman G.M. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences 105 9 (2008) 3593-3598
10. Jahnke, A., Schnauer, T., Roth, U., Mohraz, K., & Klar, H. (1997). Simulation of spiking neural networks on different hardware platforms. In Intern. conference on artificial neural networks (pp. 1187-1192) Vol. 1327
11. Jin, X., Furber, S., & Woods, J. (2008). Efficient modelling of spiking neural networks on a scalable chip multiprocessor. In IEEE international joint conference on neural networks, (pp. 2812-2819)
12. Kepecs A., van Rossum M.C.W., Song S., and Tegner J. Spike-timing-dependent plasticity: Common themes and divergent vistas. Biological Cybernetics 87 5-6 (2002) 446-458
13. Khan, M., Lester, D., Plana, L., Rast, A., Jin, X., & Painkras, E. et al. (2008). SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor. In IEEE international joint conference on neural networks (pp. 2849-2856)
14. Maass W., and Bishop C.M. Pulsed neural networks (2001), MIT Press
15. Merolla P.A., Arthur J.V., Shi B.E., and Boahen K.A. Expandable networks for neuromorphic chips. IEEE Transactions on Circuits and Systems I 54 2 (2007) 301-311
16. Morrison A., Diesmann M., and Gerstner W. Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 98 (2008) 459-478
17. Morrison A., Mehring C., Geisel T., Aertsen T.G.A., and Diesmann M.A. Advancing the boundaries of High-Connectivity network simulation with distributed computing. Neural Computation 17 8 (2005) 1776-1801
18. Nageswaran, J. M., Dutt, N., Krichmar, J. L., Nicolau, A., & Veidenbaum, A. (2009). Efficient simulation of large-scale spiking neural networks using CUDA graphics processors. In International conference on neural networks
19. Nageswaran, J. M., Dutt, N., Wang, Y., & Delbruck, T. (2009). Computing spike-based convolution on GPUs In IEEE international symposium on circuits and systems
20. Niebur, E., & Brettle, D. (1993). Efficient simulation of biological neural networks on massively parallel supercomputers with hypercube architecture. In NIPS (pp. 904-910)
21. NVIDIA (2008). CUDA programming manual version 2.0. See appendix a for technical specifications
22. Plesser, H., Eppler, J., Morrison, A., Diesmann, M., & Gewaltig, M. (2007). Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers. In Euro-Par 2007 parallel processing (pp. 672-681)
23. Song S., and Abbott L.F. Cortical development and remapping through spike timing-dependent plasticity. Neuron 32 2 (2001) 339-350
24. Song S., Miller K.D., and Abbott L.F. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3 9 (2000) 919-926
25. Thorpe S., Delorme A., and Rullen R.V. Spike-based strategies for rapid processing. Neural Networks 14 6-7 (2001) 715-725
26. Vogelstein R.J., Mallik U., Culurciello E., Cauwenberghs G., and Etienne-Cummings R. A multichip neuromorphic system for spike-based visual information processing. Neural Computation 19 9 (2007) 2281-2300