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A canonical neural mechanism for behavioral variability

Author

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  • Ran Darshan

    (ELSC, The Hebrew University of Jerusalem, Israel, Edmond Jacob Safra Campus
    Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes)

  • William E. Wood

    (Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes)

  • Susan Peters

    (Trinity College of Arts and Sciences, Duke University)

  • Arthur Leblois

    (Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes)

  • David Hansel

    (Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes)

Abstract

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5–6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these ‘universal’ statistics.

Suggested Citation

  • Ran Darshan & William E. Wood & Susan Peters & Arthur Leblois & David Hansel, 2017. "A canonical neural mechanism for behavioral variability," Nature Communications, Nature, vol. 8(1), pages 1-13, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15415
    DOI: 10.1038/ncomms15415
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    Cited by:

    1. Christopher M. Kim & Arseny Finkelstein & Carson C. Chow & Karel Svoboda & Ran Darshan, 2023. "Distributing task-related neural activity across a cortical network through task-independent connections," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    2. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Isabelle Steymans & Luciana M Pujol-Lereis & Björn Brembs & E Axel Gorostiza, 2021. "Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-17, August.
    4. Itamar Daniel Landau & Haim Sompolinsky, 2018. "Coherent chaos in a recurrent neural network with structured connectivity," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-27, December.

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