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

Publication ,  Journal Article
Lim, S; McKee, JL; Woloszyn, L; Amit, Y; Freedman, DJ; Sheinberg, DL; Brunel, N
Published in: Nat Neurosci
December 2015

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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Published In

Nat Neurosci

DOI

EISSN

1546-1726

Publication Date

December 2015

Volume

18

Issue

12

Start / End Page

1804 / 1810

Location

United States

Related Subject Headings

  • Temporal Lobe
  • Neurons
  • Neurology & Neurosurgery
  • Male
  • Macaca mulatta
  • Learning
  • Animals
  • Action Potentials
  • 5202 Biological psychology
  • 3209 Neurosciences
 

Citation

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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. Nat Neurosci, 18(12), 1804–1810. https://doi.org/10.1038/nn.4158
Lim, Sukbin, Jillian L. McKee, Luke Woloszyn, Yali Amit, David J. Freedman, David L. Sheinberg, and Nicolas Brunel. “Inferring learning rules from distributions of firing rates in cortical neurons.Nat Neurosci 18, no. 12 (December 2015): 1804–10. https://doi.org/10.1038/nn.4158.
Lim S, McKee JL, Woloszyn L, Amit Y, Freedman DJ, Sheinberg DL, et al. Inferring learning rules from distributions of firing rates in cortical neurons. Nat Neurosci. 2015 Dec;18(12):1804–10.
Lim, Sukbin, et al. “Inferring learning rules from distributions of firing rates in cortical neurons.Nat Neurosci, vol. 18, no. 12, Dec. 2015, pp. 1804–10. Pubmed, doi:10.1038/nn.4158.
Lim S, McKee JL, Woloszyn L, Amit Y, Freedman DJ, Sheinberg DL, Brunel N. Inferring learning rules from distributions of firing rates in cortical neurons. Nat Neurosci. 2015 Dec;18(12):1804–1810.

Published In

Nat Neurosci

DOI

EISSN

1546-1726

Publication Date

December 2015

Volume

18

Issue

12

Start / End Page

1804 / 1810

Location

United States

Related Subject Headings

  • Temporal Lobe
  • Neurons
  • Neurology & Neurosurgery
  • Male
  • Macaca mulatta
  • Learning
  • Animals
  • Action Potentials
  • 5202 Biological psychology
  • 3209 Neurosciences