Change search
ReferencesLink to record
Permanent link

Direct link
Biological evaluation of a Hebbian-Bayesian learning rule
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
2001 (English)In: Neurocomputing, ISSN 0925-2312, 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
URN: urn:nbn:se:kth:diva-20687ISI: 000169129200058OAI: diva2:339383
QC 20100525Available from: 2010-08-10 Created: 2010-08-10Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Lansner, Anders
By organisation
Numerical Analysis and Computer Science, NADA
In the same journal

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 35 hits
ReferencesLink to record
Permanent link

Direct link