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Long-Term Potentiation: Effects on Synaptic Coding

  • Ricci Ieong
  • Michael Stiber

Abstract

Learning, in the context of individual neurons, is represented in both physiological and Artificial Neural Network (ANN) models by change of synaptic strength. Many of the changes in neural responses as a result of the learning process may be explainable by simple consideration of synaptic strength alteration. For instance, sensitization and habituation of vertebrate and invertebrate neurons are usually interpreted in terms of incrementation and decrementation of synaptic strength [1]. However, there are still many unexplained cases [2]. In the mammalian hippocampus, neurons do not receive one stimulus, but a number of different stimuli from a variety of different locations. So simply looking at the increment and decrement of synaptic strength is insufficient to explain what has been modified by learning.

Keywords

Bifurcation Diagram Neural Response Synaptic Strength Interspike Interval Inhibitory 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]
    J. Martinez, Jr. and R. Kesner, Learning and Memory: A Biological View. Florida: Academic Press, 1986.Google Scholar
  2. [2]
    T. Berger, G. Chauvet, and R. Sclabassi, “A biologically based model of functional properties of the hippocampus,” Neural Networks, vol. 7, no. 6/7, pp. 1031–1064, 1994.Google Scholar
  3. [3]
    T. Bliss and T. Lomo, “Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path,” The Journal ot Physiology, vol. 232, pp. 331–356, 1973.Google Scholar
  4. [4]
    S. Charpier, Y. Oda, and H. Kom, “Long-term enhancement of inhibitory synaptic transmission in the control nervous system,” in Long Terni Potentiation 2 (M. Baudry and J. Davis, eds.), pp. 151–168, MIT PRESS, 1995.Google Scholar
  5. [5]
    S. Shinomoto, M. Crair, M. Tsukada, and T. Aihara, “The stimulus dependent induction of long-term potentiation in cal area of the hippocampus ii: Mathematical model,” tech. rep., Department of Information-Communication Engineering, Tamagawa University, Machida, 194 Japan, 1992.Google Scholar
  6. [6]
    J. Segundo, E. Altshuler, M. Stiber, and A. Garfinkel, “Periodic inhibition of living pacemaker neurons: I. locked, intermittent, messy, and hopping behaviors,” lui. J. Bi%mration and Chaos, vol. I. pp. 549–81, Sept. 1991.CrossRefGoogle Scholar
  7. [7]
    M. Stiber and J. P. Segundo, “Dynamics of synaptic transfer in living and simulatied neurons,” in Proc.’CNN-93, (San Francisco), pp. 75–80, Mar. 1993.Google Scholar
  8. [8]
    M. Stiber and R. leong, “Hysteresis and asymmetric sensitivity to change in pacemaker responses to inhibitory input transients,” in Brain Processes, Theories and Models. W.S. McCulloch: 25 Years in Memoriam (R. Moreno-Diaz and J. Mira-Mira, eds.), (Grand Canary, Spain), pp. 513 - -22, Nov. 1995.Google Scholar
  9. [9]
    M. Stiber and J. Segundo, Learning in neural models with complex dynamics, in Oroc. CNN-93, (Nagoya, Japan), pp. 405–8, 1993.Google Scholar
  10. [10]
    T. Bliss and G. Collingridge, “A synaptic model of memory: Long-term potentiation in the hippocampus,” Nature, vol. 361, pp. 31–39, Jan. 1993.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  1. 1.Department of Computer ScienceThe Hong Kong University of Science and TechnologyKowloonHong Kong

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