Developing a network-based understanding of Drosophila larvae locomotion using computational neuroscience and live imaging of neural activity

Lead Research Organisation: University of St Andrews
Department Name: Psychology


Understanding how neural networks implement behavioural decision-making is a fundamental goal of neuroscience. In this studentship, you will apply cutting-edge techniques in neurogenetics and live imaging to explore how a locomotor-related central pattern generator (CPG) selects and implements motor programmes. You will perform live imaging of CPG activity using genetically-encoded calcium indicators in Drosophila larvae. You will then apply and develop computational neuroscience tools for the analysis of live imaging data. Your aims will be to 1) uncover how the architecture of locomotor networks governs how different types of motor patterns are initiated, maintained, and modified and 2) develop new computational methods for 'mining' of large live imaging datasets in neuroscience. You will work with Bayesian network inference algorithms, a powerful computational methodology for revealing network structure. Bayesian network algorithms have been applied successfully to several types of neural electrophysiology data but not yet to live imaging data, thus you will pioneer this approach.

This project is unique in that it will be jointly supervised by both a Drosophila motor systems researcher (SRP) and a Computation neuroscientist (AS). The project will be based in Scotland, but depending on student interest, may involve international travel and collaboration with neuroscientists at Janelia Research Campus and Cold Spring Harbor Laboratories in the United States. This project is a CASE studentship. It will also involve working with Cairn Instruments, a leading optoelectronics company based in Kent, UK.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M010996/1 01/10/2015 30/09/2023
2024565 Studentship BB/M010996/1 27/09/2017 26/09/2021 Jacob Ridley Francis