Weight dependence in BCM leads to adjustable synaptic competition

Albert Albesa‐González, Maxime Froc, Oliver Williamson, Mark C. W. van Rossum (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)431-444
Number of pages14
JournalJournal of Computational Neuroscience
Volume50
Issue number4
Early online date29 Jun 2022
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Synaptic plasticity
  • BCM
  • learning rule
  • STDP

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