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A fundamental goal of systems neuroscience is to understand how neural activity gives rise to natural behavior. In order to achieve this goal, we must first build comprehensive models that offer quantitative descriptions of behavior. We develop a new class of probabilistic models to tackle this challenge in the study of larval zebrafish, an important model organism for neuroscience. Larval zebrafish locomote via sequences of punctate swim bouts--brief flicks of the tail--which are naturally modeled as a marked point process. However, these sequences of swim bouts belie a set of discrete and continuous internal states, latent variables that are not captured by standard point process models. We incorporate these variables as latent marks of a point process and explore various models for their dynamics. To infer the latent variables and fit the parameters of this model, we develop an amortized variational inference algorithm that targets the collapsed posterior distribution, analytically marginalizing out the discrete latent variables. With a dataset of over 120,000 swim bouts, we show that our models reveal interpretable discrete classes of swim bouts and continuous internal states like hunger that modulate their dynamics. These models are a major step toward understanding the natural behavioral program of the larval zebrafish and, ultimately, its neural underpinnings.
Author Information
Anuj Sharma (Imagen Technologies)
Robert Johnson (Harvard University)
I am a PhD student in the Program in Neuroscience at Harvard University studying larval zebrafish behavior in the lab of Florian Engert. Toward this end, I designed and built a robot which tracks and records high resolution video of a larval zebrafish swimming freely in a large arena. Together with colleagues, we construct models to predict zebrafish behavior given its current environmental input, behavioral history, and internal hunger state.
Florian Engert (Harvard University)
Scott Linderman (Columbia University)
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