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Biological evaluation of a Hebbian-Bayesian learning rule
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2358-7815
2001 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 38, 433-438 p.Article in journal (Refereed) Published
Abstract [en]

A correlation based Hebbian-Bayesian learning rule formulated on theoretical, probabilistic grounds has been extended to an incremental version running in continuous time and with spiking units. This learning rule has, however, not previously been evaluated in any detail with regard to biological plausibility and ability to mimic synaptic long-term potentiation and depression. It is demonstrated here that this learning rule indeed captures several fundamental aspects of Hebbian spike-timing dependent synaptic plasticity. A slightly modified version of the model gives a quantitative fit to data.

Place, publisher, year, edition, pages
2001. Vol. 38, 433-438 p.
Keyword [en]
synaptic plasticity model, BCPNN, spike-timing dependency, long-term potentiation, hippocampal slices, neurons, memory, model, mechanism
Identifiers
URN: urn:nbn:se:kth:diva-20687ISI: 000169129200058OAI: oai:DiVA.org:kth-20687DiVA: diva2:339383
Note
QC 20100525Available from: 2010-08-10 Created: 2010-08-10 Last updated: 2017-12-12Bibliographically approved

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