On-line Hebbian learning for spiking neurons: Architecture of the weight-unit of NESPINN

  • Ulrich Roth
  • Axel Jahnke
  • Heinrich Klar
Part VIII: Implementations
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


We present the implementation of on-line Hebbian learning for NESPINN, the Neurocomputer for the simulation of spiking neurons. In order to support various forms of Hebbian learning we developed a programmable weight unit for the NESPINN-system. On-line weight modifications are performed event-controlled in parallel to the computation of basic neuron functions. According to our VHDL-simulations, the system will offer a performance of up to 50 MCUPS.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. Jahnke, U. Roth, H. Klar: “A SIMD/Dataflow Architecture for a Neurocomputer for Spike-Processing Neural Networks (NESPINN)”, MicroNeuro'96, 232–237,1996.Google Scholar
  2. 2.
    G. Frank, G. Hartmann, “An artificial Neural Network Accelerator for Puls-coded Modelneurons”, ICNN'95, 1995.Google Scholar
  3. 3.
    M.Rossmann, Vost, K.Goser, A,Buehlmeier, G.Manteuffel,“Exponential Hebbian On-Line Learning Implemented in FPGAs”, ICANN'96, pp. 767–772,1996.Google Scholar
  4. 4.
    B.Ruf, M.Schmitt,“Learning Temporally Encoded Patterns in Networks of Spiking Neurons”, IWANN'97, in press.Google Scholar
  5. 5.
    P. Koenig, W. Schillen, “Stimulus-dependent assembly formation of oscillatory responses: III.Learning”, in Neural Computation, 4: 666–681, 1992.Google Scholar
  6. 6.
    O. Sporns, G. Tononi, G. M. Edelman, “Modelling perceptual grouping and figure-ground segregation by means of active reentrant connections”, Proc. Mad. Acad. Sci. USA, 88: 129–133,1991.Google Scholar
  7. 7.
    C. von der Malsburg, W. Schneider, “A neural cocktail-party processor”, Biol. Cybern. 54: 29–40,1986.Google Scholar
  8. 8.
    R. Eckhom, H. J. Reitboeck, M. Arndt, P. Dicke, “Feature linking via stimulus-evoked oscillations: Experimental results from cat visual cortex and functional implication from a network model”, Proc. ICNN I: 723–730, 1989.Google Scholar
  9. 9.
    W. Gerstner, R. Ritz, J. L. van Hemmen, “A biologically motivated and analytically soluble model of collective oscillations in the cortex”, Biol. Cybern. 68: 363–374, 1993.Google Scholar
  10. 10.
    K.D. Miller and D.J.C. MacKay, “The Role of Constraints in Hebbian Learning”, Neural Computation 6(1), 100–126, 1994.Google Scholar
  11. 11.
    L. Watts, “Event-Driven Simulation of Networks of Spiking Neurons”, Advances in Neural Information Processing Systems 6: 927–934,1994.Google Scholar
  12. 12.
    J. Lazarro, J. Wawrzynek, “Silicon Auditory Processors as Computer Peripherals”, Advances in Neural Information Processing Systems 5: 820–827, 1993.Google Scholar
  13. 13.
    A. Jahnke, U. Roth, H. Klar, “Towards Efficient Hardware for Spike-Processing Neural Networks”,. World Congress on Neural Networks, 460–463, 1995.Google Scholar
  14. 14.
    A Jahnke, T. Schoenauer, U. Roth, K. Mohraz,H. Klar, “Simulation of Spiking Neural Networks on Different Hardware Platforms”, ICANN'97.Google Scholar
  15. 15.
    U. Roth, F.Eckardt, A.Jahnke, H.Klar,“Efficient On-Line Computation of Connectivity: Architecture of the Connection Unit of NESPINN”, submitted to MicroNeuro `97.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Ulrich Roth
    • 1
  • Axel Jahnke
    • 1
  • Heinrich Klar
    • 1
  1. 1.Institut für MikroelektronikTechnische Universität BerlinBerlin

Personalised recommendations