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Weight dependence in BCM leads to adjustable synaptic competition

Albesa-González, Albert; Froc, Maxime; Williamson, Oliver; van Rossum, Mark C.W.

Weight dependence in BCM leads to adjustable synaptic competition Thumbnail


Albert Albesa-González

Maxime Froc

Oliver Williamson

Chair and Director/Neural Computation Research Group


Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields.


Albesa-González, A., Froc, M., Williamson, O., & van Rossum, M. C. (2022). Weight dependence in BCM leads to adjustable synaptic competition. Journal of Computational Neuroscience, 50(4), 431-444.

Journal Article Type Article
Acceptance Date Jun 8, 2022
Online Publication Date Jun 29, 2022
Publication Date 2022-11
Deposit Date Jun 29, 2022
Publicly Available Date Jun 29, 2022
Journal Journal of Computational Neuroscience
Print ISSN 0929-5313
Electronic ISSN 1573-6873
Publisher Springer Science and Business Media LLC
Peer Reviewed Peer Reviewed
Volume 50
Issue 4
Pages 431-444
Keywords Cellular and Molecular Neuroscience; Cognitive Neuroscience; Sensory Systems
Public URL
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