A key challenge is scale -- how can we relate the responses of large neuronal populations to complex stimuli and behavior that are themselves high-dimensional? Computation is crucial; it lets us model and interpret the functional relationship between stimulus, neuronal response, and behavior, and it helps us design stimuli and experiments to test specific hypotheses about neuronal coding.
In my PhD work, I focused on the visual system of the primate. Working at multiple levels with multiple techniques -- from the retina to extra-striate cortex, using physiology and behavior -- I elucidated key principles of visual encoding. In the visual cortex, I identified novel forms of visual coding using controlled, naturalistic stimuli, and in the retina, I characterized nonlinearities in ganglion cells using online, large-scale model fitting and targeted stimulus delivery.
I plan to combine my computational approaches to data analysis and experimental design with the rich variety of tools available in genetic model organisms.
Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.