From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

S. Karpenko , S. Wolf , J. Lafaye , G. Le Goc , T. Panier , V. Bormuth , R. Candelier , G. Debrégeas

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eLife, 9
Published 02 Jan. 2020
DOI: 10.7554/eLife.52882
ISSN: 2050-084X

Abstract

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predict the stationary distribution of the fish's body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can similarly capture the statistics of both spontaneous and contrast-driven navigation.

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