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Physical Mapping of Spiking Neural Networks Models on a Bio-inspired Scalable Architecture

  • J. Manuel Moreno
  • Javier Iglesias
  • Jan L. Eriksson
  • Alessandro E. P. Villa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

The paper deals with the physical implementation of biologically plausible spiking neural network models onto a hardware architecture with bio-inspired capabilities. After presenting the model, the work will illustrate the major steps taken in order to provide a compact and efficient digital hardware implementation of the model. Special emphasis will be given to the scalability features of the architecture, that will permit the implementation of large-scale networks. The paper will conclude with details about the physical mapping of the model, as well as with experimental results obtained when applying dynamic input stimuli to the implemented network.

Keywords

Neural Network Model Neuron Model Hardware Realization Spike Neural Network Spike Timing Dependent Plasticity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Maas, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Networks 10, 1659–1671 (1997)CrossRefGoogle Scholar
  2. 2.
    Hill, S.L., Villa, A.E.P.: Dynamic transitions in global network activity influenced by the balance of excitation and inhibition. Network: Computation in Neural Systems 8, 165–184 (1997)CrossRefGoogle Scholar
  3. 3.
    Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nature Neuroscience 3, 1178–1183 (2000)CrossRefGoogle Scholar
  4. 4.
    Bell, C.C., Han, V.Z., Sugawara, Y., Grant, K.: Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 387, 278–281 (1997)CrossRefGoogle Scholar
  5. 5.
    Montgomery, J.M., Madison, D.V.: Discrete synaptic states define a major mechanism of synapse plasticity. Trends in Neurosciences 27, 744–750 (2004)CrossRefGoogle Scholar
  6. 6.
    Eriksson, J., Torres, O., Mitchell, A., Tucker, G., Lindsay, K., Halliday, D., Rosenberg, J., Moreno, J.M., Villa, A.E.P.: Spiking Neural Networks for Reconfigurable POEtic Tissue. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 165–173. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Torres, O., Eriksson, J., Moreno, J.M., Villa, A.E.P.: Hardware optimization and serial implementation of a novel spiking neuron model for the POEtic tissue. BioSystems 76, 201–208 (2003)CrossRefGoogle Scholar
  8. 8.
    Moreno, J.M., Thoma, Y., Sanchez, E., Torres, O., Tempesti, G.: Hardware Realization of a Bio-inspired POEtic Tissue. In: Proceedings of the NASA/DoD Conference on Evolvable Hardware, pp. 237–244. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. Manuel Moreno
    • 1
  • Javier Iglesias
    • 2
  • Jan L. Eriksson
    • 2
  • Alessandro E. P. Villa
    • 3
  1. 1.Dept. of Electronic EngineeringTechnical University of CatalunyaBarcelonaSpain
  2. 2.Laboratory of Neuroheuristics, Information Systems Department INFORGEUniversity of LausanneLausanneSwitzerland
  3. 3.INSERM U318University Joseph-Fourier Grenoble 1GrenobleFrance

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