Inferring learning rules from distributions of firing rates in cortical neurons.

Published

Journal Article

Information about external stimuli is thought to be stored in cortical circuits through experience-dependent modifications of synaptic connectivity. These modifications of network connectivity should lead to changes in neuronal activity as a particular stimulus is repeatedly encountered. Here we ask what plasticity rules are consistent with the differences in the statistics of the visual response to novel and familiar stimuli in inferior temporal cortex, an area underlying visual object recognition. We introduce a method that allows one to infer the dependence of the presumptive learning rule on postsynaptic firing rate, and we show that the inferred learning rule exhibits depression for low postsynaptic rates and potentiation for high rates. The threshold separating depression from potentiation is strongly correlated with both mean and s.d. of the firing rate distribution. Finally, we show that network models implementing a rule extracted from data show stable learning dynamics and lead to sparser representations of stimuli.

Full Text

Duke Authors

Cited Authors

  • Lim, S; McKee, JL; Woloszyn, L; Amit, Y; Freedman, DJ; Sheinberg, DL; Brunel, N

Published Date

  • December 2015

Published In

Volume / Issue

  • 18 / 12

Start / End Page

  • 1804 - 1810

PubMed ID

  • 26523643

Pubmed Central ID

  • 26523643

Electronic International Standard Serial Number (EISSN)

  • 1546-1726

Digital Object Identifier (DOI)

  • 10.1038/nn.4158

Language

  • eng

Conference Location

  • United States