Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation1–5. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardised stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches.
Recently, in experimental settings to probe defensive actions6 or understand the implementation of neuromodulation in small neural neworks7, larva populations exhibited significant deviations from usual behavioural features. These deviations lead to either the redefinition of features usually associated with these behaviours or to challenges in detecting them.
We aim to develop a Bayesian Program Synthesis8 (BPS) methodology for producing synthetic data that mirrors key characteristics found in experimental recordings. This method will entail an inference phase to identify large-scale parameters characteristic of larva populations. It will incorporate various generative models to either replicate the behavioural traits associated with particular genotypes or create bespoke behavioural features. Additionally, we plan to use a collection of small generative programs to create larva data under specific action conditions. The efficacy of this approach will be demonstrated by applying our behaviour analysis pipeline to the synthetic data and assessing its ability to facilitate transfer learning with annotated behavioral datasets.
Fig 1) Example of artificial generated from a pre-defined larva genotype
6. Lehman, M. et al. Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva. 2023.12.24.573276 Preprint at https://doi.org/10.1101/2023.12.24.573276 (2023).
7. Tredern, E. de et al. Feeding-state dependent modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila. 2023.12.26.573306 Preprint at https://doi.org/10.1101/2023.12.26.573306 (2023).
Scientific or technical background required for work program
The successful intern should have one of the following backgrounds:
- Statistical Physics, Applied Mathematics,
- Statistics & Bayesian Inference
Some fluency in Python is expected.
dbc-epi-recrutement at pasteur dot fr