Project Summary How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This understanding and characterization will be critical towards the application of principled neurostimulation to specific brain regions to study the effect of neural circuit perturbations on behavior, and conversely towards predictions of the neural activity during perturbations in the behavior. The research objective of this BRAIN Initiative proposal is to develop biologically-inspired goal- and data- driven artificial intelligence methods to elucidate the neurodynamical basis of sensorimotor control. The outcomes of this research program will fundamentally impact our understanding of the neural circuits underlying sensorimotor control. The tools developed herein will be disseminated for free use by the scientific community. Through this BRAIN Initiative proposal, we will elucidate principles of sensorimotor control by incorporating recorded neural data in succinct and interpretable biologically-inspired models of the relationships between the measured biological data and the corresponding behavior. In Aims 1 and 2, we will design and disseminate a comprehensive modeling framework that integrates large-scale neural and behavioral data with physics-based modeling of the musculoskeletal system and neuroanatomical constraints. The neural data and neuroanatomical constraints will be incorporated in recurrent neural network models that will achieve desired behavior through biophysically-based musculoskeletal models using cutting-edge machine learning methods. The modeling framework created here will provide a much-needed opportunity to design a virtual laboratory in Aim 3 that tests the effect of neural stimulation in a feedback setting, and predicts the effect of unseen behavioral conditions and behavioral perturbation on resulting neural activity. Breakneck advances in hardware and machine learning techniques have led to vast improvements in our ability to record and model large-scale multi-regional neural data. Our broad research goal is to advance the current state-of-the-art for modeling the neural control of movements by incorporating large-scale measurements and biological constraints into theoretical models of sensorimotor control. This is a critical step towards (a) elucidating the computational role of neural activity from different brain regions in controlling complex behavior, (b) allowing us to further refine theoretical models of movement generation based on data, and (c) understanding where and how to stimulate the brain in order to efficiently apply neurostimulation for achieving desired behavior. With the development and dissemination of these tools, we hope to enter an era where virtual laboratories are not just a way to analyze previously performed experiments, but are integrated into experimental pipelines such that they can be utilized to their full potential during large-scale neuroscience experiments.