Interpretable machine learning for understanding the neural control of movement

Modern machine learning can fit complex models that make accurate predictions. However, understanding the brain with standard machine learning is a great challenge. A critical impediment is that it is often difficult to understand the inner workings of these models. There is thus a great need for more interpretable machine learning methods for neuroscience, in which the inner workings of the model relate to neural structure or function. The proposed research will develop interpretable machine learning methods for studying the neural control of movement. Through close collaboration with the experimental lab of Dr. Churchland, these methods will be applied to answer three central questions in motor control. In the first two aims, the research investigates how flexibly spinal motor neurons can be controlled, and how activity in motor cortex controls muscle activity across a wide range of movements. Beyond increasing scientific knowledge, answering these two questions could enhance brain computer interfaces for restoring control to those with motor impairments. In the third aim, the research investigates how multiple neural populations interact with each other to plan and generate movement. This question is relevant to many neurological disorders, in which communication between brain areas is disrupted. During the K99 phase of this award, occurring within Columbia’s vibrant neuroscience community, the applicant will receive additional training in computational techniques from Drs. Paninski and Cunningham, and additional experimental training from Dr. Churchland. This training will provide the necessary experience for the applicant to be successful in this work and to successfully start an independent lab at the interface of machine learning and the neural control of movement.