Convergence in an Adaptive Neural Network: The Influence of Noise Inputs Correlation

  • Adel Daouzli
  • Sylvain Saïghi
  • Michelle Rudolph
  • Alain Destexhe
  • Sylvie Renaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)

Abstract

This paper presents a study of convergence modalities in a small adaptive network of conductance-based neurons, receiving input patterns with different degrees correlation . The models for the neurons, synapses and plasticity rules (STDP) have a common biophysics basis. The neural network is simulated using a mixed analog-digital platform, which performs real-time simulations. We describe the study context, and the models for the neurons and for the adaptation functions. Then we present the simulation platform, including analog integrated circuits to simulate the neurons and a real-time software to simulate the plasticity. We also detail the analysis tools used to evaluate the final state of the network by the way of its post-adaptation synaptic weights. Finally, we present experimental results, with a systematic exploration of the network convergence when varying the input correlation, the initial weights and the distribution of hardware neurons to simulate the biological variability.

Keywords

Neuromorphic Engineering Silicon Neurons STDP  Hodgkin-Huxley Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hebb, D.O.: The Organization of Behaviour. John Wiley and Sons, Chichester (1949)Google Scholar
  2. 2.
    Markram, H., Lubke, J., Frotscher, M., Sackmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997)CrossRefGoogle Scholar
  3. 3.
    Song, S., Abbott, L.: Cortical development and remapping through spike timing-dependent plasticity. Neuron 32, 339–350 (2001)CrossRefGoogle Scholar
  4. 4.
    van Rossum, M.C.W., Turrigiano, G.: Correlation based learning from spike timing dependent plasticity. Neurocomputing 38-40, 409–415 (2001)CrossRefGoogle Scholar
  5. 5.
    Daouzli, A., Saïghi, S., Buhry, L., Bornat, Y., Renaud, S.: Weights convergence and spikes correlation in an adaptive neural network implemented on vlsi. In: Proc.s of the Int. Conf. on Bio-inspired Systems and Signal Processing (BIOSIGNALS), pp. 286–291 (2008)Google Scholar
  6. 6.
    Zou, Q., Bornat, Y., Saïghi, S., Tomas, J., Renaud, S., Destexhe, A.: Analog-digital simulations of full-conductance-based networks of spiking neurons with spike timing dependent plasticity. Network: computation in neural systems 17, 211–233 (2006)CrossRefGoogle Scholar
  7. 7.
    Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 500–544 (1952)CrossRefGoogle Scholar
  8. 8.
    Connors, B., Gutnick, M.: Intrinsic firing patterns of diverse neocortical neurons. Trends in Neurosciences 13, 99–104 (1990)CrossRefGoogle Scholar
  9. 9.
    Destexhe, A., Mainen, Z., Sejnowski, T.J.: An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation 6, 10–14 (1994)CrossRefGoogle Scholar
  10. 10.
    Badoual, M., Zou, Q., Davison, A.P., Rudolph, M., Bal, T., Frégnac, Y., Destexhe, A.: Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int. J. Neural Syst. 16(2), 79–98 (2006)CrossRefGoogle Scholar
  11. 11.
    Froemke, R.C., Dan, Y.: Spike-timing-dependent plasticity modification induced by natural spike trains. Nature 416, 433–438 (2002)CrossRefGoogle Scholar
  12. 12.
    Renaud, S., Tomas, J., Bornat, Y., Daouzli, A., Saighi, S.: Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks. In: InternationaI Symposium on Circuits And Systems, pp. 3355–3358. IEEE, Los Alamitos (2007)Google Scholar
  13. 13.
    Hines, M.L., Carnevale, N.T.: The neuron simulation environment. Neural Computation 9, 1179–1209 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Adel Daouzli
    • 1
  • Sylvain Saïghi
    • 1
  • Michelle Rudolph
    • 2
  • Alain Destexhe
    • 2
  • Sylvie Renaud
    • 1
  1. 1.IMS LabsUniversity of BordeauxTalenceFrance
  2. 2.UNIC - CNRSGif-sur-YvetteFrance

Personalised recommendations