WEIGHTS CONVERGENCE AND SPIKES CORRELATION IN AN ADAPTIVE NEURAL NETWORK IMPLEMENTED ON VLSI

A. Daouzli, S. Saïghi, L. Buhry, Y. Bornat, S. Renaud

2008

Abstract

This paper presents simulations of a conductance-based neural network implemented on a mixed hardware-software simulation system. Synaptic connections follow a bio-realistic STDP rule. Neurons receive correlated input noise patterns, resulting in a weights convergence in a confined range of conductance values. The correlation of the output spike trains depends on the correlation degree of the input patterns.

References

  1. Abbott, L. F. and Nelson, S. B. (2000). Synaptic plasticity: taming the beast. Natural Neuroscience, 3:1178- 1183.
  2. Badoual, M., Zou, Q., Davison, A. P., Rudolph, M., Bal, T., Frégnac, Y., and Destexhe, A. (2006). Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int. J. Neural Syst., 16(2):79-98.
  3. Connors, B. and Gutnick, M. (1990). Intrinsic firing patterns of diverse neocortical neurons. Trends in Neurosciences, 13:99-104.
  4. Destexhe, A., Mainen, Z., and Sejnowski, T. J. (1994). An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation, 6:10-14.
  5. Feldman, D. E. (2000). Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27:45-56.
  6. Froemke, R. C. and Dan, Y. (2002). Spike-timingdependent plasticity modification induced by natural spike trains. Nature, 416:433-438.
  7. Froemke, R. C., Poo, M., and Dan, Y. (2005). Spiketiming-dependent plasticity depends on dendritic location. Nature, 434:221-225.
  8. Hebb, D. O. (1949). The Organization of Behaviour. John Wiley & Sons.
  9. Hines, M. L. and Carnevale, N. T. (1997). The neuron simulation environment. Neural Computation, 9:1179- 1209.
  10. Hodgkin, A. L. and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117:500-544.
  11. Kepecs, A., van Rossum, M. C. W., Song, S., and Tegner, J. (2002). Spike timing dependent plasticity: common themes and divergent vistas. Biological Cybernetics, 87:446-458.
  12. Le Masson, G., Renaud-Le Masson, S., Debay, D., and Bal, T. (2002). Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature, 417:854-858.
  13. Lewis, N., Bornat, Y., Alvado, L., Lopez, C., Daouzli, A., Levi, T., Tomas, J., Saghi, S., and Renaud, S. (2006). Pax : un outil logiciel / matériel d'investigation pour les neurosciences computationnelles. In NeuroComp, pages 171-174.
  14. Magee, J. C. and Johnston, D. (1997). A synaptically controlled, associative signal for hebbian plasticity in hippocampal neurons. Science, 275:209-213.
  15. Markram, H., Lubke, J., Frotscher, M., and Sackmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275:213-215.
  16. Renaud, S., Tomas, J., Bornat, Y., Daouzli, A., and Saighi, S. (2007). Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks. In InternationaI Symposium on Circuits And Systems, pages 3355-3358. IEEE.
  17. Roberts, P. D. and Bell, C. C. (2002). Spike timing dependent synaptic plasticity in biological systems. Biological Cybernetics, 87:392-403.
  18. Rumsey, C. C. and Abbott, L. F. (2003). Equalization of synaptic efficacy by activity- and timing-dependent synaptic plasticity. The Journal of Neurophysiology, 91:2273-2280.
  19. Singer, W. and Gray, C. M. (1995). Visual feature integration and the temporal correlation hypothesis. Annual Review of Neuroscience, 18:555-586.
  20. Song, S. and Abbott, L. (2001). Cortical development and remapping through spike timing-dependent plasticity. Neuron, 32:339-350.
  21. Song, S., Miller, K., and Abbott, L. (2000). Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3:919-926.
  22. van Rossum, M. C. W., Bi, G.-Q., and Turrigiano., G. (2000). Stable hebbian learning from spike timingdependent plasticity. The Journal of Neuroscience, 20:8812-8821.
  23. van Rossum, M. C. W. and Turrigiano, G. (2001). Correlation based learning from spike timing dependent plasticity. Neurocomputing, 38-40:409-415.
  24. Zou, Q. (2006). Computational models of spike timing dependent plasticity: synapses, neurons and circuits. PhD thesis, Université Paris VI.
  25. Zou, Q., Bornat, Y., Saïghi, S., Tomas, J., Renaud, S., and Destexhe, A. (2006a). Analog-digital simulations of full-conductance-based networks of spiking neurons with spike timing dependent plasticity. Network: computation in neural systems, 17:211-233.
  26. Zou, Q., Bornat, Y., Tomas, J., Renaud, S., and Destexhe, A. (2006b). Real-time simulations of networks of hodgkin-huxley neurons using analog circuits. Neurocomputing, 69:1137-1140.
  27. Zou, Q. and Destexhe, A. (2007). Kinetic models of spiketiming dependent plasticity and their functional consequences in detecting correlations. Biol. Cybern., 97(1):81-97.
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Paper Citation


in Harvard Style

Daouzli A., Saïghi S., Buhry L., Bornat Y. and Renaud S. (2008). WEIGHTS CONVERGENCE AND SPIKES CORRELATION IN AN ADAPTIVE NEURAL NETWORK IMPLEMENTED ON VLSI . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 286-291. DOI: 10.5220/0001069102860291


in Bibtex Style

@conference{biosignals08,
author={A. Daouzli and S. Saïghi and L. Buhry and Y. Bornat and S. Renaud},
title={WEIGHTS CONVERGENCE AND SPIKES CORRELATION IN AN ADAPTIVE NEURAL NETWORK IMPLEMENTED ON VLSI},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={286-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001069102860291},
isbn={978-989-8111-18-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - WEIGHTS CONVERGENCE AND SPIKES CORRELATION IN AN ADAPTIVE NEURAL NETWORK IMPLEMENTED ON VLSI
SN - 978-989-8111-18-0
AU - Daouzli A.
AU - Saïghi S.
AU - Buhry L.
AU - Bornat Y.
AU - Renaud S.
PY - 2008
SP - 286
EP - 291
DO - 10.5220/0001069102860291