Building a platform for large-scale neuroscience


Abstract: Neuroscience is quickly entering the realm of big data. New technologies for monitoring neural activity are yielding large, complex, high-dimensional data, in some cases reaching 1 or 10 TB per experiment. These data demand entirely new approaches to analysis. The open-source platform Spark is particularly well-suited to analyzing neural data, because it supports iterative computation, it enables interactive, exploratory analysis, and it has a powerful, elegant API that streamlines the development of complex analyses. I will show how we have adapted Spark to the demands of neural data. I will introduce the library we have developed, Thunder, and describe how it implements several large-scale analyses suitable for neural data. I will also discuss ongoing challenges, including strategies for visualization, the development of a standardized format for neural data, and the extension of our analyses to the real-time, streaming setting. Together, our work paves the way towards a general-purpose, open-source framework for large-scale neuroscience.


Jeremy Freeman is a neuroscientist who uses computation to understand the brain. He obtained his BA from Swarthmore College in math, biology, and psychology, and completed a PhD in neural science at New York University. Currently at HHMI's Janelia Farm Research Campus, Freeman develops new approaches for analyzing, visualizing, and manipulating large-scale patterns of neural activity in animals — flies, fish, and mice — while they perform complex behaviors. He hopes to reveal principles according to which all brains function, including our own.