Interpretable machine learning for understanding the neural control of movement

While the development of many new neural recording technologies has led to much excitement, it has created a huge gap between our ability to collect data and our ability to deeply understand it. More interpretable machine learning tools are necessary for understanding neural function in health and neurological disease, to one day develop curative interventions. The goal of this research is to develop interpretable machine learning models and apply them to understand how brain areas interact with each other and how they control muscle activity during a wide range of movements.