A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping.

ABSTRACT: 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. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to grasp objects would give insight into the computations of the grasping circuit. Internal activity of modular networks trained with these constraints strongly resembled neural activity recorded from the g

SUBMITTER: Michaels JA 

PROVIDER: S-EPMC7749336 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature