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In-Silico Model of NMDA and Non-NMDA Receptor Activities Using Analog Very-Large-Scale Integrated Circuits

  • Guy Rachmuth
  • Chi-Sang Poon
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 551)

Abstract

Computational modeling is a useful analytical tool that (with valid data) may help to explain how complex brain systems work. In respiratory control, various neural network models have been proposed to explore the possible mechanisms of respiratory rhythmogenesis based on intrinsic neuronal properties as well as interactions at the network level1,2. However, the large parameter space that must be tested for such neuromorphic models makes it rather cumbersome and time consuming to simulate even a modest size network with discrete respiratory neurons. For large-scale neural network models involving multiple populations of respiratory neurons, such simulations become computationally prohibitive on general-purpose digital computers.

Keywords

Very Large Scale Integration Respiratory Neuron Hebbian Learning Rule AMPAR Channel Very Large Scale Integration Circuit 
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.
    Rybak, I.A., Shevtsova, N.A., St-John, W.M., Paton, J.F.R., Pierrefiche, O. Endogenous rhythm generation in the pre-Botzinger complex and ionic currents: modeling and in vitro studies, European Journal of Neuroscience 18(2): 239–257 (2003).CrossRefPubMedGoogle Scholar
  2. 2.
    Butera, R.J., Johnson, S.M., DelNegro, C.A., Rinzel, J., Smith, J.C. Dynamics of excitatory networks of bursting pacemaking neurons: Modeling and experimental studies of the respiratory central pattern generator. Neurocomputing: 32: 323–330 (2000).CrossRefGoogle Scholar
  3. 3.
    Poon, C.-S. and M. Siniaia. Plasticity of cardiorespiratory neural processing: classification and computational functions. Respirat. Physiol. Special Issue on Modeling and Control of Breathing 122: 83–109 (2000).CrossRefGoogle Scholar
  4. 4.
    Mead., C. Analog VLSI and Neural Systems. 1989. Addison-Wesley. Reading Massachusetts.Google Scholar
  5. 5.
    Mahowald, M. and R. Douglas, A Silicon neuron. Nature 354: pp. 515–518, (1991).CrossRefPubMedGoogle Scholar
  6. 6.
    Hebb, D. The Organization of Behavior. 1949, New York Wiely.Google Scholar
  7. 7.
    Young, D., and C.S. Poon. A Hebbian feedback covariance learning paradigm for self-tuning optimal control. IEEE Tran Systems and Cybernetics 31: 173–186 (2001).CrossRefGoogle Scholar
  8. 8.
    Rachmuth, G. and C.S. Poon. Design of a neuromorphic hebbian synapse using analog VLSI. First International IEEE EMBS Conference on Neural Engineering, Capri, Italy, 20–22 March 2003 Conference Proceedings., pp. 221–224 (2003).Google Scholar
  9. 9.
    Mussa-Ivalidi, F.A., and Miller, L.E. Brain-Machine interfaces: computational demands and clinical needs meet basic neuroscience. Trends in Neuroscience. 26: 329–334 (2003).CrossRefGoogle Scholar
  10. 10.
    Jung, R., Brauer, E.J., Abbas, J.J. Real-time interaction between a neuromorphic electronic circuit and the spinal cord. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 9: 319–326 (2001).CrossRefPubMedGoogle Scholar
  11. 11.
    Krebs, H. Hogan, N., Aisen, M.L., and Volpe, T. Robot-aided neurorehabilitation. IEEE Trans Rehabil Engineering, 6: 75–87 (1998).CrossRefGoogle Scholar
  12. 12.
    Sutton, R.S., and Barto, A.G. Reinforcement Learning. (1998). Cambridge, Massachusetts, MIT Press.Google Scholar

Copyright information

© Kluwer Academic/Plenum Publishers, New York 2004

Authors and Affiliations

  • Guy Rachmuth
    • 1
    • 2
  • Chi-Sang Poon
    • 1
  1. 1.Harvard-MIT Division of Health Sciences and TechnologyMITCambridge
  2. 2.Division of Engineering and Applied SciencesHarvard UniversityCambridge

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