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Skilled reaching tasks for head-fixed mice using a robotic manipulandum.


ABSTRACT: Skilled forelimb behaviors are among the most important for studying motor learning in multiple species including humans. This protocol describes learned forelimb tasks for mice using a two-axis robotic manipulandum. Our device provides a highly compact adaptation of actuated planar two-axis arms that is simple and inexpensive to construct. This paradigm has been dominant for decades in primate motor neuroscience. Our device can generate arbitrary virtual movement tracks, arbitrary time-varying forces or arbitrary position- or velocity-dependent force patterns. We describe several example tasks permitted by our device, including linear movements, movement sequences and aiming movements. We provide the mechanical drawings and source code needed to assemble and control the device, and detail the procedure to train mice to use the device. Our software can be simply extended to allow users to program various customized movement assays. The device can be assembled in a few days, and the time to train mice on the tasks that we describe ranges from a few days to several weeks. Furthermore, the device is compatible with various neurophysiological techniques that require head fixation.

SUBMITTER: Wagner MJ 

PROVIDER: S-EPMC7586302 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Skilled reaching tasks for head-fixed mice using a robotic manipulandum.

Wagner Mark J MJ   Savall Joan J   Kim Tony Hyun TH   Schnitzer Mark J MJ   Luo Liqun L  

Nature protocols 20200207 3


Skilled forelimb behaviors are among the most important for studying motor learning in multiple species including humans. This protocol describes learned forelimb tasks for mice using a two-axis robotic manipulandum. Our device provides a highly compact adaptation of actuated planar two-axis arms that is simple and inexpensive to construct. This paradigm has been dominant for decades in primate motor neuroscience. Our device can generate arbitrary virtual movement tracks, arbitrary time-varying  ...[more]

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