Inferring learning rules from distributions of firing rates in cortical neurons.
Publication/Presentation Date
12-1-2015
Abstract
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.
Volume
18
Issue
12
First Page
1804
Last Page
1810
ISSN
1546-1726
Published In/Presented At
Lim, S., McKee, J. L., Woloszyn, L., Amit, Y., Freedman, D. J., Sheinberg, D. L., & Brunel, N. (2015). Inferring learning rules from distributions of firing rates in cortical neurons. Nature neuroscience, 18(12), 1804–1810. https://doi.org/10.1038/nn.4158
Disciplines
Medicine and Health Sciences | Pediatrics
PubMedID
26523643
Department(s)
Department of Pediatrics
Document Type
Article