A canonical neural mechanism for behavioral variability

Ran Darshan, William E. Wood, Susan Peters, Arthur Leblois and David Hansel ()
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Ran Darshan: ELSC, The Hebrew University of Jerusalem, Israel, Edmond Jacob Safra Campus
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

Nature Communications, 2017, vol. 8, issue 1, 1-13

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15415

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DOI: 10.1038/ncomms15415

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