Supervisor: Mark Humphries (School of Psychology)
Co-supervisor: Ruediger Thul (School of Mathematics)

Description:

A wealth of systems can be described by weighted networks, in which nodes represent parts of the system and links represent interactions between nodes. Crucially, the strength of interaction is represented by the weight of the link. The brain, and circuits within the brain, are a prime example of such networks, with the synaptic connections between brain regions and between individual neurons creating weighted links. Many scientific questions can be formulated as the problem of finding specific types of structure within a system’s weighted network.

This project will introduce and explore a new class of model weighted networks, “divergent” networks. In these networks the structure described by the links and their weights do not match. We will:

1. Create generative models for these networks, which can tune the degree of divergence between the links and weights for a range of structures, including network communities
2. Quantify the existence and extent of divergence in a range of real-world networks, with a particular focus on connectomes at both single neuron and inter-area level (including human cortical networks)
3. Explore how such divergence alters a network’s dynamics across a range of simple dynamical systems implemented on the nodes

Requirements:

• Matlab and/or Python programming experience. A strong quantitative background in e.g. physics, mathematics, computer science or engineering

Please see full job listing here.