Project ID: SST_CIRC_6
The objective of this interdisciplinary project is to study neuroscientific data using tools from mathematics, data science and computer science.
A connectome is a map of neural connections in a brain. Historically a significant problem in neuroscience has been the acquisition of neuron scale connectomic data. However, due to technological advances, using a combination of lab based biological experiments, neuroimaging and computational methods, the amount of data available is growing rapidly. This has resulted in a new problem of how to analyse this vast quantity of data.
The project is primarily interested in considering connectomes as a network (or graph), where each neuron represents a node of our network and each synapse represents a link between these nodes. Using tools from network science and graph theory we can analysis and understand the structure of these brain networks. In particular, the project will explore the frequency and distribution of cycles, that is paths through neurons that start and end at the same neuron. The student will investigate various structural properties of these cycles, such as their location, structure, frequency and their links to known biological properties of the brain, such as memory. They will also investigate functional properties of these cycles, such as how they affect the propagation of information
through the brain and the robustness of the network. A variety of connectomes will be investigated looking at different scales and species, all using existing neuroscientific data.
This project will require the student to learn topics from a wide range of fields. Neuroscientific theory will be important in trying explain how patterns in brain networks link to biological phenomenon. Network and graph theory from mathematics will be needed to develop theoretical results describing the behaviour of these structures, and develop appropriate control models. Algorithmic theory and advanced programming techniques will be used to develop approaches for finding patterns in these large sets of data.
The student will work closely with Dr Jason Smith (NTU), as well as collaborators at the Blue Brain Project (EPFL) in Switzerland and other institutions. The student will be expected to publish in high quality, peer-reviewed, interdisciplinary journals and to disseminate research at relevant seminars and international conferences.
Informal enquiries are encouraged and should be addressed to Dr Jason Smith
For the eligibility criteria, visit our studentship application page.
How to apply
To make an application, please visit our studentship application page.
Application deadline: Friday 12 January 2024, closing at 12 pm.
Fees and funding
This is part of NTU's 2024 fully-funded PhD Studentship Scheme.
Guidance and support
Application guidance can be found on our studentship application page.