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

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 16 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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Preprints, Working Papers, ...
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Contributor : Georges Debregeas <>
Submitted on : Monday, December 16, 2019 - 12:15:55 PM
Last modification on : Tuesday, September 22, 2020 - 3:48:05 AM
Long-term archiving on: : Tuesday, March 17, 2020 - 4:34:50 PM


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Sophia Karpenko, Sébastien Wolf, Julie Lafaye, Guillaume Le Goc, Volker Bormuth, et al.. From behavior to circuit modeling of light-seeking navigation in zebrafish larvae. 2019. ⟨hal-02413703⟩



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