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

Citation

Debregeas, Georges et al. (2020), From behavior to circuit modeling of light-seeking navigation in zebrafish larvae, Dryad, Dataset, https://doi.org/10.5061/dryad.v9s4mw6qx

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.

Methods

Data acquisition and pre-processing are described in the associated manuscript, which can be found at:

https://www.biorxiv.org/content/10.1101/810960v1

Usage Notes

Contains Data and Matlab codes for analysis of orientational light-seeking behavior experiments analysis

Data Pooled

Folder 'Data_pooled' contains matlab datafiles with the sequences of navigation under different modalities (see below). Each line is one trajectory. AngleSource is the fish's body angle to the virtual light source, expressed in radian. AngleLab is the fish's body angle in the frame of reference of the laboratory. xCoord and yCoord are the fish center of mass position expressed in pixel size (11,5 px = 1mm). Time of bout (TimeBout) is measured from the onset of the recording sequence, in seconds. FishN is the number of individual fish.

Different modalities :

- No stimulus (Figure 1) :
    'spontaneous_swim.mat'
- Stereo-visual/Contrast-driven orientational phototaxis (Figure 2) :
    'lateralized_exps.mat'
- Temporal orientational phototaxis (Figure 3) :
    'temporal_exps.mat'
- Temporal orientational phototaxis with enucleated fish (Figure 3 supplement) :
    'enucleated_exps.mat'

Programs

Folder 'Programs' contains matlab scripts and functions in following folders:

- Matlab routines used to analyse the fish trajectories in no stimulus (Figure 1) modality:
    'Spontaneous'
- Matlab routines used to analyse the fish trajectories in stereo-visual/Contrast-driven orientational phototaxis (Figure 2) modality :
    'Lateralized'
- Matlab routines used to analyse the fish trajectories in temporal orientational phototaxis (Figure 3) :
    'Temporal'  
- Matlab routines used to analyse the fish trajectories in temporal orientational phototaxis with enucleated fish (Figure 3 supplement) :
    'Enucleated'
- Matlab routines used to analyse the fish trajectories for behavioral (biased) random walk simulations (Figure 4) :
    'Simulations/BehavioralModel'
- Matlab routines used to analyse the fish trajectories for neural model simulations (Figure 5) :
    'Simulations/NeuralModel'

Funding

Human Frontier Science Program, Award: RGP0060/2017

H2020 European Research Council, Award: 71598

Agence Nationale de la Recherche, Award: ANR-16-CE16-0017

Fondation pour la Recherche Médicale, Award: FDT201904008219