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ICANN 98 pp 117-122 | Cite as

Queuing theory for spike driven synaptic dynamics

  • Mario Annunziato
  • Stefano Fusi
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

We present a model for spike driven, stochastic dynamics of a synapse with two stable states, suited for aVLSI implementation. The stochastic nature of learning, which allows for optimal storage capacity, is due to the variability in spike emission times of pre- and post-synaptic neurons, and emerges as a result of the collective properties of the whole network. The dynamics of the single synapse is studied with the methods of queuing theory. Numerical results show that LTP and LTD are stochastically induced by the two neurons’ activity states and transition probabilities are in the range required by the theory of stochastic learning.

Keywords

Synaptic Efficacy Stochastic Transition Material Device Synaptic Dynamic Single Synapse 
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]
    Amit D.J. and Fusi S. Dynamic learning in neural networks with material synapses, Neural Computation 6 957 (1994); Brunei N., Carusi F. and Fusi S., Slow stochastic Hebbian learning of classes of stimuli in a recurrent NN, Network, 9, 123–152 (1998)CrossRefGoogle Scholar
  2. [2]
    Badoni D., Bertazzoni S., Buglioni S., Salina G., Amit D.J. and Fusi S., Electronic implementation of an analog NN with stochastic transitions, Network, 6 125 (1995)CrossRefGoogle Scholar
  3. [3]
    D.J. Amit and N. Brunei, 1997. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex, Cerebral Cortex 7 No. 3, 237–252.CrossRefGoogle Scholar
  4. [4]
    Fusi S. and Mattia M. Collective behavior of networks with linear (VLSI) integrate-and-fire neurons, submitted to Neural Computation, (1998); Mattia M., Del Giudice P., Amit D. J., Asynchronous simulation of large networks of spiking neurons and dynamical synapses, these proceedings Google Scholar
  5. [5]
    C. A. van Vreeswijk and H. Sompolinsky, Chaos in neural networks with balanced excitatory and inhibitory activity Science, 274 No. 5293, 1724–1726 (1996)CrossRefGoogle Scholar
  6. [6]
    Annunziato M., Badoni D., Fusi S., Salamon A., Analog VLSI implementation of a spike-driven stochastic dynamical synapse, these proceedings Google Scholar
  7. [7]
    C. Mead, Analog VLSI and Neural System, Reading, MA: Addison-Wesley (1989)CrossRefGoogle Scholar
  8. [8]
    Cox D. R. and Miller H. D., The Theory of Stochastic Processes, London: METHUEN & CO LTD (1965)MATHGoogle Scholar

Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Mario Annunziato
    • 1
  • Stefano Fusi
    • 2
  1. 1.Physics Dept.University of PisaItaly
  2. 2.INFN Sezione RM1RomeItaly

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