title: Inferring learning rules from distributions of firing rates in cortical neurons.
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.
authors.affiliation: Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.
authors.forename: Sukbin
authors.initials: S
authors.lastname: Lim
authors.affiliation: Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.
authors.forename: Jillian L
authors.initials: JL
authors.lastname: McKee
authors.affiliation: Department of Neuroscience, Columbia University, New York, New York, USA.
authors.forename: Luke
authors.initials: L
authors.lastname: Woloszyn
authors.affiliation: Department of Statistics, University of Chicago, Chicago, Illinois, USA.|Department of Computer Science, University of Chicago, Chicago, Illinois, USA.
authors.forename: Yali
authors.initials: Y
authors.lastname: Amit
authors.affiliation: Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Freedman
authors.affiliation: Department of Neuroscience, Brown University, Providence, Rhode Island, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Sheinberg
authors.affiliation: Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.|Department of Statistics, University of Chicago, Chicago, Illinois, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Brunel
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D007858:Learning / Q000502:physiology*; D008253:Macaca mulatta; D008297:Male; D009474:Neurons / Q000502:physiology*; D013702:Temporal Lobe / Q000166:cytology / Q000502:physiology*