Advertisement

Real-Time Spiking Neural Network: An Adaptive Cerebellar Model

  • Christian Boucheny
  • Richard Carrillo
  • Eduardo Ros
  • Olivier J. -M. D. Coenen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

Abstract

A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in real-time on a dual-processor computer. The model is implemented on an event-driven spiking neural network simulator with table-based conductance and voltage computations. The cerebellar model interacts every millisecond with a time-driven simulation of a simple environment in which adaptation experiments are setup. Learning is achieved in real-time using spike time dependent plasticity rules, which drive synaptic weight changes depending on the neurons activity and the timing in the spiking representation of an error signal. The cerebellar model is tested on learning to continuously predict a target position moving along periodical trajectories. This setup reproduces experiments with primates learning the smooth pursuit of visual targets on a screen. The model learns effectively and concurrently different target trajectories. This is true even though the spiking rate of the error representation is very low, reproducing physiological conditions. Hence, we present a complete physiologically relevant spiking cerebellar model that runs and learns in real-time in realistic conditions reproducing psychophysical experiments. This work was funded in part by the EC SpikeFORCE project (IST-2001-35271, www.spikeforce.org).

Keywords

Purkinje Cell Spike Train Smooth Pursuit Cerebellar Nucleus Periodical Trajectory 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Carrillo, R.R., Ros, E., Ortigosa, E.M., Barbour, B., Agís, 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)CrossRefGoogle Scholar
  2. 2.
    Kettner, R.E., Mahamud, S., Leung, H., Sittkoff, N., Houk, J.C., Peterson, B.W., Barto, A.G.: Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement. Journal of Neurophysiology 77(4), 2115–2130 (1997)Google Scholar
  3. 3.
    Ito, M.: Cerebellar long-term depression: characterization, signal transduction, and functional roles. Physiological Reviews 81(3), 1143–1195 (2001)Google Scholar
  4. 4.
    Kuroda, S., Yamamoto, K., Miyamoto, H., Doya, K., Kawato, M.: Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning. Biological Cybernetics 84, 183–192 (2001)CrossRefGoogle Scholar
  5. 5.
    Hansel, C., Linden, D.J., D’Angelo, E.: Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum. Nature Neuroscience 4(5), 467–475 (2001)Google Scholar
  6. 6.
    Coenen, O.J.M.D., Arnold, M.P., Sejnowski, T.J., Jabri, M.A.: Parallel fiber coding in the cerebellum for life-long learning. Autonomous Robots 11(3), 291–297 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Gerstner, W., Kistler, W.M.: Spiking neuron models. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  8. 8.
    Lev-Ram, V., Mehta, S.B., Kleinfeld, D., Tsien, R.Y.: Reversing cerebellar long-term depression. Proceedings of the National Academy of Sciences 100(26), 15989–15993 (2003)CrossRefGoogle Scholar
  9. 9.
    Medina, J.F., Nores, W.L., Mauk, M.D.: Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses. Nature 416, 330–333 (2003)CrossRefGoogle Scholar
  10. 10.
    Schweighofer, N., Doya, K., Fukai, H., Chiron, J.V., Furukawa, T., Kawato, M.: Chaos may enhance information transmission in the inferior olive. Proceedings of the National Academy of Sciences 101, 4655–4660 (2004)CrossRefGoogle Scholar
  11. 11.
    Schweighofer, N., Arbib, A.A., Kawato, M.: Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum. European Journal Of Neuroscience 10, 95–105 (1998)CrossRefGoogle Scholar
  12. 12.
    Spoelstra, J., Schweighofer, N., Arbib, M.A.: Cerebellar learning of accurate predictive control for fast-reaching movements. Biological Cybernetics 82, 321–333 (2000)CrossRefGoogle Scholar
  13. 13.
    Medina, J.F., Mauk, M.D.: Simulations of cerebellar motor learning: computational analysis of plasticity at the mossy fiber to deep nucleus synapse. The Journal of Neuroscience 19(16), 7140–7151 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christian Boucheny
    • 1
  • Richard Carrillo
    • 2
  • Eduardo Ros
    • 2
  • Olivier J. -M. D. Coenen
    • 1
  1. 1.Sony Computer Science Laboratory ParisParisFrance
  2. 2.Department of Computer Architecture and Technology, E.T.S.I. InformáticaUniversity of GranadaGranadaSpain

Personalised recommendations