Understanding how the brain represents information is essential to developing therapeutic treatments for brain damage and disorders.
Computational models of complex neural networks allow us to understand how different components are contributing to properties of the brain. An important emerging theory is that the geometry of relative activity within a neuronal population represents information. But many computational models lack key biological features limiting their utility and testability of this idea.
As a 2024 Schmidt Science Fellow, Hayley plans to build biologically-grounded, testable models to understand the contributions of different types of neurons to the population representation of information in the brain.
Interdisciplinary Pivot: Biological Sciences to Math & Computer Science