Improving the language for describing neural computation

While dynamical-systems theory provides a universal language for studying change, the field often overemphasizes vector-field topology (e.g., fixed points) as the primary interpretive framework. The language we use to describe dynamical mechanisms constrains which solutions we consider plausible and which phenomena we can interpret. We identify and incorporate mathematical concepts that overcome current limitations, and we test them on real neural and behavioral data.

We develop mathematical tools to turn high-dimensional noisy data into interpretable insights.