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.
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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