A canonical neural mechanism for behavioral variability.

Ran Darshan, William E. Wood, Susan Peters, Arthur Leblois, David Hansel
Nat Commun. 2017-05-22; 8(1):
DOI: 10.1038/ncomms15415

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Darshan R(1)(2), Wood WE(2), Peters S(3), Leblois A(2), Hansel D(2).

Author information:
(1)ELSC, The Hebrew University of Jerusalem, Israel, Edmond Jacob Safra Campus, Givat Ram 91904, Jerusalem.
(2)Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes, 45 Rue des Saints Pères, Paris 75270, France.
(3)Trinity College of Arts and Sciences, Duke University, Durham, North Carolina 27708, USA.

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.

DOI: 10.1038/ncomms15415
PMCID: PMC5458148
PMID: 28530225 [Indexed for MEDLINE]

Auteurs Bordeaux Neurocampus