Project Grant R01DA060790

Award Date 5/1/25
Completion Date 3/31/28
Dollars Obligated $379K
Federal Grant Program
93.279
Assistance Type
Project Grant
Place of Performance
Colorado, USA
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  1. A computational model based on optimal control theory to estimate muscle activity from kinematics and electromyography data, which can help reduce the number of animal experiments required.

  2. Tools to predict mouse reaching kinematics and muscle activity when changes are made to the task or limb biomechanics, which will further enhance experimental efficiency.

  3. Demonstrations of how the musculoskeletal model and physics engine can be used to extract biomechanical features and correlate them with neural activity in the motor cortex and cerebellum, to test hypotheses about motor control.

  4. Synthesis of feedback controllers using artificial neural networks with deep reinforcement learning, allowing researchers to implement and observe the effects of hypothesized control architectures.

The project will run from May 1, 2025 to March 31, 2028, and the tools will be made freely available to the neuroscience community with extensive documentation and support.

Generated 5/13/25, 3:27 AM