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Transformation of Context-dependent Sensory Dynamics into Motor Behavior

Overview of attention for article published in PLoS Computational Biology, February 2013
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Title
Transformation of Context-dependent Sensory Dynamics into Motor Behavior
Published in
PLoS Computational Biology, February 2013
DOI 10.1371/journal.pcbi.1002908
Pubmed ID
Authors

Roberto Latorre, Rafael Levi, Pablo Varona

Abstract

The intrinsic dynamics of sensory networks play an important role in the sensory-motor transformation. In this paper we use conductance based models and electrophysiological recordings to address the study of the dual role of a sensory network to organize two behavioral context-dependent motor programs in the mollusk Clione limacina. We show that: (i) a winner take-all dynamics in the gravimetric sensory network model drives the typical repetitive rhythm in the wing central pattern generator (CPG) during routine swimming; (ii) the winnerless competition dynamics of the same sensory network organizes the irregular pattern observed in the wing CPG during hunting behavior. Our model also shows that although the timing of the activity is irregular, the sequence of the switching among the sensory cells is preserved whenever the same set of neurons are activated in a given time window. These activation phase locks in the sensory signals are transformed into specific events in the motor activity. The activation phase locks can play an important role in motor coordination driven by the intrinsic dynamics of a multifunctional sensory organ.

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 3%
Spain 1 3%
United States 1 3%
France 1 3%
Unknown 27 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 26%
Student > Ph. D. Student 7 23%
Student > Master 4 13%
Student > Bachelor 4 13%
Professor 3 10%
Other 2 6%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 29%
Neuroscience 6 19%
Computer Science 2 6%
Engineering 2 6%
Physics and Astronomy 1 3%
Other 3 10%
Unknown 8 26%