Abstract
A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components. We focus on conductance-based models for neurons that emulate the temporal dynamics of the synaptic integration process. We have designed an efficient computing architecture using reconfigurable hardware in which the different stages of the neuron model are processed in parallel (using a customized pipeline structure). Further improvements occur by computing multiple neurons in parallel using multiple processing units. The computing platform is described and its scalability and performance evaluated. The goal is to investigate biologically realistic models for the control of robots operating within closed perception-action loops.
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References
Mattia, M., Guidice, P.D.: Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Computation 12, 2305–2329 (2000)
Reutimann, J., Guigliano, M., Fusi, S.: Event-driven simulation of spiking neurons with stochastic dynamics. Neural Computation 15, 811–830 (2003)
Delorme, A., Thorpe, S.: SpikeNET: An event-driven simulation package for modelling large networks of spiking neurons. Network: Computation in Neural Systems 14, 613–627 (2003)
Eckhorn, R., Bauer, R., Jordan, W., Brosh, M., Kruse, W., Munk, M., Reitboeck, H.J.: Coherent oscillations: A mechanism of feature linking in the visual cortex? Biol. Cyber. 60, 121–130 (1988)
Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex. Neural Computation 2, 293–307 (1990)
Gerstner, W., Kistler, W.: Spiking Neuron Models. University Press, Cambridge (2002)
Jahnke, A., Schoenauer, T., Roth, U., Mohraz, K., Klar, H.: Simulation of Spiking Neural Networks on Different Hardware Platforms. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 1187–1192. Springer, Heidelberg (1997)
Hartmann, G., Frank, G., Schaefer, M., Wolff, C.: SPIKE128K- An Accelerator for Dynamic Simulation of Large Pulse-Coded Networks. In: MicroNeuro 1997, pp. 130–139 (1997)
Shaefer, M., Schoenauer, T., Wolff, C., Hartmann, G., Klar, H., Rueckert, U.: Simulation of Spiking Neural Networks – architectures and implementations. Neurocomputing 48, 647–679 (2002)
Janke, A., Roth, U., Klar, H.: A SIMD/dataflow architecture for a neurocomputer for spike processing neural networks (NESPINN). In: Proc. MicroNeuro 1996, pp. 232–237 (1996)
Schoenauer, T., Atasoy, S., Mehrtash, N., Klar, H.: NeuroPipe-Chip: A Digital Neuro-Processor for Spiking Neural Networks. IEEE Trans. Neural Networks 13(1), 205–213 (2002)
Mehrtash, N., Jung, D., Hellmich, H.H., Schoenauer, T., Lu, V.T., Klar, H.: Synaptic Plasticity in Spiking Neural Networks (SP2INN): A System Approach. IEEE Transactions on Neural Networks 14(5) (2003)
Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via stimulus evoked oscillations: Experimental results from cat visual cortex and functional implication from a network model. In: Proc. ICNN I, pp. 723–720 (1989)
Hill, J., McColl, W., Stefanescu, D., Goudreau, M., Lang, K., Rao, S., Suel, T., Tsantilas, T., Bisseling, R.: BSPlib: the BSP Programming Library. Parallel Computing 24(14), 1947–1980 (1998)
Celoxica (2001-2004), [Online] Available http://www.celoxica.com
Xilinx (1994-2003), [Online] Available http://www.xilinx.com
Arnold, M.: Feedback learning in the olivary-cerebellar system: PhD Thesis, The University of Sydney (2001)
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Ros, E., Ortigosa, E.M., Agís, R., Carrillo, R., Prieto, A., Arnold, M. (2005). Spiking Neurons Computing Platform. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_58
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DOI: https://doi.org/10.1007/11494669_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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