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Nat Commun. 2019 Oct 23;10(1):4812. doi: 10.1038/s41467-019-12670-z.

Context-dependent limb movement encoding in neuronal populations of motor cortex.

Author information

1
Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland. omlor@hifo.uzh.ch.
2
Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland. omlor@hifo.uzh.ch.
3
Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
4
Laboratory of Neural Regeneration and Repair, Brain Research Institute, University of Zurich, and Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
5
Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
6
Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland.
7
Scientific IT Services, ETH Zurich, CH-8092, Zurich, Switzerland.
8
Institut de la Vision, 75012, Paris, France.
9
Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland. helmchen@hifo.uzh.ch.
10
Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland. helmchen@hifo.uzh.ch.

Abstract

Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. Yet it remains unclear how encoding of joint movement in M1 depends on varying environmental contexts. Using calcium imaging we measured neuronal activity in layer 2/3 of the M1 forelimb region while mice grasped regularly or irregularly spaced ladder rungs during locomotion. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from individual joints in the regular and irregular context, even for equivalent grasping actions across conditions. This context-dependence of M1 encoding emerged during task learning, fostering higher precision of grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). These findings suggest that M1 exploits information from M2 to adapt encoding of joint movements to the flexibility demands of distinct familiar contexts, thereby increasing the accuracy of motor output.

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