Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales

Abstract

Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their neural implementation, natural behavior must be carefully measured and quantified. Here we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout-types emitted over time. We then construct probabilistic models — specifically, marked renewal processes — to evaluate how bout-types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated worlds. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety. This dissertation describes a statistical framework to evaluate the principal output of the larval zebrafish nervous system — i.e. natural behavior. As efforts to map the development, structure, and function of this system continually improve, methods to measure and model its output will become increasingly important, both to constrain biologically plausible neural circuit models and to gain mechanistic insight into general principles of neural computation. The scientific community has begun what is likely to be a many-centuries-long mission to emulate brains of research animals inside semi-realistic simulations of the natural world. Enormous resources will be poured into this problem, and larval zebrafish are strong contenders to be among the first model organisms for which convincing synapse-scale simulations of entire vertebrate neuromuscular systems are developed. The general purpose behavioral analysis framework described here represents one of many early steps toward this very distant scientific goal.