Behaviour-driven optimisation of neural connectivity. Starting 1 October 2019

The PhD student will join an interdisciplinary team of experts in optimisation and computational neuroscience to answer an extremely important question: how does the neural connectivity determine behaviours? To answer this question, the student will design computational models of neural networks, and use advanced machine learning and optimisation techniques to find the correspondence between connectivity and functionality.

Information processing in the brain, and ultimately observed behaviour, are based on the communication between spiking neurons that are embedded in a network of synaptic connections. In nature, different individual organisms usually have different connectivity (represented by a graph of interconnected nodes) that can produce the same behaviour. It is therefore not obvious how to locate optimal neural connectivity to match a desirable behaviour. In this exciting project, we aim to develop a novel approach towards solving this optimisation problem.

The connectivity graph can be described in terms of connection probabilities, which should be optimised to generate a target behaviour. This is a very important problem, but the computational complexity renders traditional optimisation algorithms impractical. Thus, we expect Bayesian optimisation to be an efficient approach in this context. However, this method must be adapted for graph-based problems. In this project, we will explore various uncertainty quantification methods for graph connectivity to enable the use of Bayesian optimisation. To demonstrate the performance and test the efficacy of our methods, we will study the motor behaviours of a simple animal for which neurobiological data and effective models are available.

Applicants should have (at least) a first or upper second class honours degree in computer science, mathematics, or a related discipline. A relevant MSc or MRes qualification is desirable.

A strong educational background and relevant skills are required: for example, knowledge of optimisation, graph theory and multi-variate calculus, and experience of software development using C++, Python or MATLAB. It would be beneficial for the student to have experience with machine learning or statistics, and knowledge of computational neuroscience.

If you wish to discuss this project further informally, please contact Dr Alan Millard. However, applications must be made in accordance with the details shown below.

General information about applying for a research degree at the University of Plymouth.

You can apply via the online application form and selecting ‘Apply’.

Please mark it FAO Carole Watson, clearly stating that you are applying for a PhD studentship within the School of Computing, Electronics and Mathematics. Please attach a covering letter detailing your suitability for the studentship, a CV, Research statement, and 2 academic references.

For more information on the admissions process contact Carole Watson.

The closing date for applications is 12 noon on 31 May 2019. Shortlisted candidates will be invited for interview mid June. We regret that we may not be able to respond to all applications. Applicants who have not received an offer of a place by 30 June 2019 should consider their application has been unsuccessful on this occasion.