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

Sophia Karpenko (1) , Sébastien Wolf (1) , Julie Lafaye , Guillaume Le Goc , Volker Bormuth (2) , Raphaël Candelier (3) , Georges Debrégeas (4)

1 LJP - Laboratoire Jean Perrin
2 LJP - Laboratoire Jean Perrin
3 LPS - Laboratoire de Physique Statistique de l'ENS
4 Laboratoire Jean PERRIN

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.

Domaines: Neurosciences [q-bio.NC]

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⟩