Mapping the brain at scale


Abstract: Complex animal behavior reflects computation across the entire nervous system, involving the coordinated activity of thousands of neurons within and across multiple brain areas. New technologies are making it possible to monitor neural function during behavior at unprecedented scales. But the massive size and complexity of the resulting data sets has become a major bottleneck for progress. We present a solution in the form of a library, called Thunder, for fast, interactive, exploratory analysis of large-scale neural data built on the new open-source Apache Spark platform for distributed computing. Our analyses perform in minutes complex, iterative computations that would be intractable or impossibly slow with other methods. We use these analyses to characterize large-scale neural correlates of both sensory and motor behavior in the larval zebrafish, revealing both functional organization and neuronal dynamics at the scale of entire brains, and the resolution of single neurons. The open-source analytical framework we offer thus holds promise for turning brain activity mapping efforts into biological insight.


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.