RT Journal Article
SR Electronic
T1 A modular neural network model of grasp movement generation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 742189
DO 10.1101/742189
A1 Jonathan A. Michaels
A1 Stefan Schaffelhofer
A1 Andres Agudelo-Toro
A1 Hansjörg Scherberger
YR 2020
UL http://biorxiv.org/content/early/2020/02/25/742189.abstract
AB One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning 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 using visual features to generate the required muscle dynamics to grasp objects would explain the neural and computational basis of the grasping circuit. Modular networks with object feature input and sparse inter-module connectivity outperformed other models at explaining neural data and the inter-area relationships present in the biological circuit, despite the absence of neural data during network training. Network dynamics were governed by simple rules, and targeted lesioning of modules produced deficits similar to those observed in lesion studies, providing a potential explanation for how grasping movements are generated.