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An associative memory model with probabilistic synaptic transmission

Graham B & Willshaw DJ (1997) An associative memory model with probabilistic synaptic transmission. In: Bower J (ed.) Computational Neuroscience: Trends in Research, 1997. Annual Computational Neuroscience Conference, Boston, MA, USA, 14.07.1996-17.07.1996. New York: Springer, pp. 315-319.;

The associative net model of heteroassociative memory with binary-valued synapses has been extended to include recent experimental data that indicates that in the hippocampus one form of synaptic modification is a change in the probability of synaptic transmission [2]. Pattern pairs are stored in the net by a version of the Hebbian learning rule that changes the probability of transmission at synapses where the presynaptic and postsynaptic units are simultaneously active from a low, base value to a high, modified value. Numerical calculations of the expected recall response have been used to assess the performance for different values of the base and modified probabilities. If there is a cost incurred with generating the difference between these probabilities, then the optimal difference is around 0.4. Performance can be greatly enhanced by using multiple cue presentations during recall.

Author(s)Graham, Bruce; Willshaw, David J
Publication date31/12/1997
Publication date online31/07/1996
Publisher URL…1-4757-9800-5_51
Place of publicationNew York
ConferenceAnnual Computational Neuroscience Conference
Conference locationBoston, MA, USA
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