Funded postdoctoral positions available! Email Ann with inquiries.

Theories, models, and methods for understanding the neural control of complex naturalistic and learned behaviors.

---

“Science is built with facts, as a house is build with stones; but a collection of facts is no more a science than a pile of stones is a house.” – Henri Poincaré

The architecture of neural circuits is often wonderfully reflective of their function in the brain. As theoretical neuroscientists, we use mathematical modeling and model-informed analyses to understand the computational principles at work in nervous systems.

By modeling the dynamics of neural populations, we study how circuit architecture shapes learning, decision-making, and evolutionarily conserved survival behaviors. We also develop new machine learning methods to model the actions of freely behaving animals, and to relate these moment-to-moment actions to the animals’ neural activity.

Our work blends dynamical systems, data science, and machine learning to investigate neural dynamics and behavior across multiple scales, brain regions, and model organisms. We value open science, clear communication, and creation of community resources. We collaborate enthusiastically with other theorists, and with experimental groups at Northwestern and beyond.