Long-Term Potentiation: Effects on Synaptic Coding

  • Ricci Ieong
  • Michael Stiber


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.


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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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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