The student will investigate whole-brain network dynamics in navigating zebrafish larvae to learn more about the neural computations underlying navigation, and to characterize a number of brain states associated with various motor programs. Light-sheet microscopy and functional calcium imaging will be used for data collection.

Whole-brain network dynamics in zebrafish larvae during spontaneous and sensory-driven virtual navigation

Computational Neuroscience
Sorbonne University

Zebrafish larva possesses a combination of assets – small dimensions, brain transparency, genetic tractability – which makes it a unique vertebrate model system to probe brain-scale neuronal dynamics. Using light-sheet microscopy, it is currently possible to monitor the activity of the entire brain at cellular resolution using functional calcium imaging, at about 1 full brain/ second.  

The ESR7 will harness this unique opportunity to dissect the neural computation at play during sensory-driven navigation. 5-7 days old larvae will be partially restrained in agarose,  i.e. with their tail free. Real-time video-monitoring of the tail beats will be used to infer virtual navigational parameters (displacement, reorientation); visual or thermal stimuli will be delivered to the larvae in a manner that will simulate a realistic navigation along light or thermal gradients.

During this virtual sensory-driven navigation, the brain activity will be monitored using two-photon light-sheet functional imaging. These experiments will provide rich datasets of whole-brain activity during a complex sensorimotor task.

The network dynamics will be analysed in order to extract a finite number of brain states associated with various motor programs. Starting from spontaneous navigation phases (i.e. absence of varying sensory cues), ESR7 will analyse how different sensory cues interfere with the network endogenous dynamics to bias the probability of these different brain states and eventually favor movements along sensory gradients.

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.