We investigate the functional mechanisms of how the macaque parietal, premotor, and motor cortex is able to plan and execute visually guided hand and finger movements. To achieve this, we perform simultaneous recordings from three cortical areas in the macaque brain that are strongly linked to gasp movement planning and execution: the anterior intraparietal area (AIP), the ventral premotor area (PMv, also called area F5), and the hand area of primary motor cortex (M1). Together with simultaneously recorded hand kinematics using a data glove, we will model the neural population activity of AIP, F5, and M1 with a dynamical, recurrent neural network to better understand the coding properties and population dynamics of the neuronal network of these brain areas. Furthermore, we will impose motor perturbations on hand and finger movements, which will lead to neuronal adaptations in AIP, F5 and M1, and are therefore highly informative about the coding properties of the dynamical cortical network for individual finger and grasp movements. Finally, based on these network understandings and our previous experience with neural interfaces, we propose to develop high-performance real-time decoding algorithms for hand grasping. Such real-time decoders would be extremely beneficial for future neurophysiological experiments as well as potential neuroprosthetic applications. Together, this proposal aims to substantially advance our understanding of the fundamental coding principles of hand and finger movements in the primate brain as well as develop efficient real-time decoders for future closed-loop experiments and neuroprosthetic applications. 

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