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A neural network model of flexible grasp movement generation

View ORCID ProfileJonathan A. Michaels, View ORCID ProfileStefan Schaffelhofer, View ORCID ProfileAndres Agudelo-Toro, View ORCID ProfileHansjörg Scherberger
doi: https://doi.org/10.1101/742189
Jonathan A. Michaels
Deutsches Primatenzentrum GmbH, Kellnerweg 4, 37077 Goettingen, GermanyElectrical Engineering Department, Stanford University, Stanford, CA 94305, USA
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Stefan Schaffelhofer
Deutsches Primatenzentrum GmbH, Kellnerweg 4, 37077 Goettingen, Germany
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Andres Agudelo-Toro
Deutsches Primatenzentrum GmbH, Kellnerweg 4, 37077 Goettingen, Germany
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Hansjörg Scherberger
Deutsches Primatenzentrum GmbH, Kellnerweg 4, 37077 Goettingen, GermanyFaculty of Biology and Psychology, University of Goettingen, 37073 Goettingen, Germany
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  • For correspondence: hscherberger@dpz.eu
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Abstract

One of the main ways we interact with the world is using our hands. In macaques, the circuit formed by the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and trained to transform visual features into the muscle fiber velocity required to grasp objects would recapitulate neural data in the macaque grasping circuit. While a number of network architectures produced the required kinematics, modular networks with visual input and activity that was encouraged to be biologically realistic best matched neural data and the inter-area differences present in the biological circuit. Network dynamics could be explained by simple rules that also allowed the correct prediction of kinematics and neural responses to novel objects, providing a potential mechanism for flexibly generating grasping movements.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 24, 2019.
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A neural network model of flexible grasp movement generation
Jonathan A. Michaels, Stefan Schaffelhofer, Andres Agudelo-Toro, Hansjörg Scherberger
bioRxiv 742189; doi: https://doi.org/10.1101/742189
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A neural network model of flexible grasp movement generation
Jonathan A. Michaels, Stefan Schaffelhofer, Andres Agudelo-Toro, Hansjörg Scherberger
bioRxiv 742189; doi: https://doi.org/10.1101/742189

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