Motile animals use sensory cues to navigate towards environments where there are more likely to obtain food, find mates or to avoid predators. Sensory-driven navigation relies on a closed-loop mechanism between motor action and motor-induced sensory inputs. At each instant, multiple sensory cues have to be integrated to bias the forthcoming motor command.
ESR7 will thoroughly and quantitatively characterize the behavioral algorithm underlying sensory-driven navigation in zebrafish larvae. The animals will be 5-10 days old, as this age is amenable to whole-brain functional imaging. The project will focus on both phototaxis (navigation towards a light source) and thermotaxis (navigation relative to a thermal gradient).
Two experimental platforms will be set up.
1. Freely swimming larvae will be video-monitored and submitted to whole-field visual stimuli. The visual stimulation will be locked in real-time on the animal’s orientation and/or position in space. This will allow in particular to separately probe the effect of stereo (difference in illumination between both eyes) and uniform (total illumination on both eyes) visual cues. For thermally-driven navigation, the animal will be allowed to freely explore a large environment in which a constant thermal gradient is imposed.e
2. Experiments will be reproduced in a virtual-reality setting. In this case, the animal is partially restrained in agarose with its tail free. Monitoring the tail movement will provide access to its virtual displacement, on which the visual and/or thermal stimuli will be locked. This second setup will constitute the basis for the associated functional imaging experiments (see ESR8)
These behavioral experiments will be analysed in order to describe the animal’s navigation as a sensory-biased random walk.
Expected Results:
1. Two experimental platforms to quantitatively extract sensory-driven navigational parameters in zebrafish larvae, in both freely-swimming and in a virtual reality setting
2. A thorough statistical characterization of the motor sequences during sensory-driven navigation.
3. A behavioral model capturing the behavioral algorithms at play.
Planned secondment(s):
1. Julijana Gjorgjieva, MPG-BR, M14-16. Behavioral analysis.
2. Iain Couzin, MPG-O, M23-25. Data analysis and statistical characterization of motor sequences
Enrolment in Doctoral degree(s): You will be enrolled at the Laboratoire Jean Perrin, Sorbonne Université.
