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Conference Proceeding

An associative memory model with probabilistic synaptic transmission

Citation
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. http://link.springer.com/chapter/10.1007/978-1-4757-9800-5_51; https://doi.org/10.1007/978-1-4757-9800-5_51

Abstract
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.

StatusPublished
Author(s)Graham, Bruce; Willshaw, David J
Publication date31/12/1997
Publication date online31/07/1996
PublisherSpringer
Publisher URLhttp://link.springer.com/…1-4757-9800-5_51
Place of publicationNew York
ISBN978-1-4757-9802-9
ConferenceAnnual Computational Neuroscience Conference
Conference locationBoston, MA, USA
Dates
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