TY - JOUR T1 - A neural network model of flexible grasp movement generation JF - bioRxiv DO - 10.1101/742189 SP - 742189 AU - Jonathan A. Michaels AU - Stefan Schaffelhofer AU - Andres Agudelo-Toro AU - Hansjörg Scherberger Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/08/24/742189.abstract N2 - 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. ER -