This project will build a transformative bridge between data science and neuroscience. These two young fields are driving cutting-edge progress in the technology, education, and healthcare sectors, but their shared foundations and deep synergies have yet to be exploited in an integrated way - a new discipline of "data neuroscience." This integration will benefit both fields: Neuroscience is producing massive amounts of data at all levels, from synapses and cells to networks and behavior. Data science is needed to make sense of these data, both in terms of developing sophisticated analysis techniques and devising formal, mathematically rigorous theories. At the same time, models in data science involving AI and machine learning can draw insights from neuroscience, as the brain is a prodigious learner and the ultimate benchmark for intelligent behavior. Beyond fundamental scientific gains in both fields, the project will produce additional outcomes, including: new collaborations between universities, accessible workshops, graduate training, integration of undergraduate curricula in data science and neuroscience, research opportunities for undergraduates that help prepare them for the STEM workforce, academic-industry partnerships, and enhanced high-performance computing infrastructure.
The overarching theme of this project is to develop a two-way channel between data science and neuroscience. In one direction, the project will investigate how computational principles from data science can be leveraged to advance theory and make sense of empirical findings at different levels of neuroscience, from cellular measurements in fruit flies to whole-brain functional imaging in humans. In the reverse direction, the project will view the processes and mechanisms of vision and cognition underlying these findings as a source for new statistical and mathematical frameworks for data analysis. Research will focus on four related objectives: (1) Distributed processing: reconciling work on communication constraints and parallelization in machine learning with the cellular neuroscience of motion perception to develop models of distributed estimation; (2) Data representation: examining how our understanding of the different ways that the brain stores information can inform statistically and computationally efficient learning algorithms in the framework of exponential family embeddings and variational inference; (3) Attentional filtering: incorporating the cognitive concept of selective attention into machine learning as a low-dimensional trace through a high-dimensional input space, with the resulting models used to reconstruct human subjective experience from brain imaging data; (4) Memory capacity: leveraging cognitive studies and natural memory architectures to inform approaches for reducing/sharing memory in artificial learning algorithms. The inherently cross-disciplinary nature of the project will provide novel theoretical and methodological perspectives on both data science and neuroscience, with the goal of enabling rapid, foundational discoveries that will accelerate future research in these fields.
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.
