A multi-rank statistical model to determine the impact of behavioral state on navigational coding by medial entorhinal cortex

Behavioral state—such as alertness or exhaustion—dramatically impacts how our brains function. Yet, in spite of the key role that it plays in cognition, how behavioral state influences brain function remains a central mystery in neuroscience.

Working in medial entorhinal cortex (MEC)—a  neural hub for navigation in which sensory cues are integrated to support complex behavior—I will combine state-of-the-art recording technology and novel statistical methods to bridge this knowledge gap. Using virtual reality task and video recording, I will precisely measure behavior in a tightly controlled context, enabling estimation of internal state changes in a fixed environmental context. Simultaneously, I will record the activity of hundreds of neurons across MEC and thus determine how population network dynamics interact with single cell coding of navigational variables. Finally, I will devise novel statistical methods to connect my behavioral and neural measurements and capture how neural coding changes dynamically with internal state.

Together this work will robustly characterize the impact of behavioral state on navigational coding. Further, this project will lay the groundwork to explore an enduring question in neuroscience—how behavior and neural activity dynamically interact with one another.

Project Details

Funding Type:

SIGF - Graduate Fellowship

Award Year:


Lead Researcher(s):

Team Members:

Lisa Giocomo (Primary Advisor, Neurobiology)
Surya Ganguli (Co-Advisor, Applied Physics)