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Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.

Publication ,  Journal Article
Brudner, S; Pearson, J; Mooney, R
Published in: PLoS computational biology
May 2023

Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.

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Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

May 2023

Volume

19

Issue

5

Start / End Page

e1011051

Related Subject Headings

  • Vocalization, Animal
  • Male
  • Learning
  • Finches
  • Circadian Rhythm
  • Bioinformatics
  • Animals
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

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Brudner, S., Pearson, J., & Mooney, R. (2023). Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance. PLoS Computational Biology, 19(5), e1011051. https://doi.org/10.1371/journal.pcbi.1011051
Brudner, Samuel, John Pearson, and Richard Mooney. “Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.PLoS Computational Biology 19, no. 5 (May 2023): e1011051. https://doi.org/10.1371/journal.pcbi.1011051.
Brudner S, Pearson J, Mooney R. Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance. PLoS computational biology. 2023 May;19(5):e1011051.
Brudner, Samuel, et al. “Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.PLoS Computational Biology, vol. 19, no. 5, May 2023, p. e1011051. Epmc, doi:10.1371/journal.pcbi.1011051.
Brudner S, Pearson J, Mooney R. Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance. PLoS computational biology. 2023 May;19(5):e1011051.

Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

May 2023

Volume

19

Issue

5

Start / End Page

e1011051

Related Subject Headings

  • Vocalization, Animal
  • Male
  • Learning
  • Finches
  • Circadian Rhythm
  • Bioinformatics
  • Animals
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences