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Probabilistic synaptic transmission in the associative net

B Graham1, D Willshaw

  • 1Centre for Cognitive Science, Edinburgh University, 2 Buccleuch Place, Edinburgh EH8 9LW, Scotland. bruce@cns.ed.ac.uk

Neural Computation
|February 9, 1999
PubMed
Summary

This study enhances the associative net model for memory by incorporating synaptic transmission probability changes observed in the hippocampus. Optimal performance for this stochastic associative net is achieved with a probability difference of 0.4, especially with multiple cue presentations during recall.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Memory Research

Background:

  • The associative net model explains heteroassociative memory with binary synapses.
  • Synaptic modification in the hippocampus involves changes in synaptic transmission probability.

Purpose of the Study:

  • To extend the associative net model to incorporate experimentally observed changes in synaptic transmission probability.
  • To assess the performance of this stochastic associative net model for memory recall.

Main Methods:

  • The study utilizes a Hebbian learning rule to modify synaptic transmission probabilities.
  • Numerical calculations were performed to evaluate the net's recall performance.
  • The model was analyzed for varying base and modified probabilities.

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Main Results:

  • An optimal performance for the stochastic associative net was found with a probability difference of approximately 0.4.
  • This optimal difference aligns with experimentally observed magnitudes of synaptic change.
  • Enhanced recall performance was demonstrated through multiple cue presentations.

Conclusions:

  • The extended associative net model provides a more accurate representation of hippocampal memory mechanisms.
  • Synaptic transmission probability is a key factor in associative memory recall.
  • Optimizing synaptic probability changes and employing multiple cues can significantly improve memory recall.