CAREER: Building interpretable models of neural population activity through view-invariant representation learning and alignment

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What happens in the brain when we move our hand to touch a glass of water, listen to the sound of rustling leaves, or play a game of chess? In most of our experiences, perception and sensation are orchestrated through the activity of large-scale circuits of neurons distributed throughout the brain. While new advances in neural recording have expanded our ability to measure the activity of large populations of (hundreds or thousands of) neurons, parsing through neural recordings to "read out" intent or behavior is still an outstanding challenge. The goal of this CAREER proposal is to develop new machine learning methods for learning robust mappings between neural activity and complex behavior. With new approaches that can go from the brain to behavior, it will be possible to better understand neural computation, compare neural activity between individuals, and create dynamic models that capture the ever-changing nature of the brain.The project will be organized into three aims, each of which focuses on development of methods to tackle key challenges in building a mapping between the brain and behavior. In Aim 1, the project will develop new methods for learning representations from neural population activity, with a focus on building invariances through self-supervised and contrastive learning methods. In Aim 2, the project will focus on the problem of learning representations jointly across multiple neural recordings and using this technology to understand common factors and differences across individuals. In Aim 3, the project will develop approaches to extract dynamic latent factors that model the shift in representations over longer time scales and apply them to study the study of healthy aging and neurodegenerative disease. This project will develop machine learning frameworks and theory for learning robust representations from neural recordings and provide new ways to quantify changes in brain activity across individuals, over time, aging, or disease.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.