PhD Study : Discovering the rules of neural learning by computer simulations of brain biophysics and deep neural networks.

Summary

Unlike modern deep learning algorithms, the brain is a 3D physical object containing different cell components: neurons, synapses, and astrocytes, all enmeshed together in a complicated spaghetti. Their tight anatomical arrangement means that nearby cells send stronger biochemical signals to each other than cells that are far apart. Despite its likely importance on synapses and brain learning, this spatial arrangement is ignored in almost all computational models of neural plasticity, and deep neural network algorithms. As a result, current leading theories of brain learning and AI are likely missing key principles.

This project will use multiscale computer modelling methods to tackle this open problem, pushing forward our understanding of brain learning and super-charging our deep learning algorithms. You will use multiscale computer simulations of electro-chemical signalling in neurons, synapses, and astrocytes to understand how the physical arrangement of different cell types affects brain learning. You will first use highly data-constrained biophysical simulations to study the physical processes in detail. Based on the results of these simulations, you will then use more abstract deep neural network models to study the possible computational function of these mechanisms for learning and memory.

During this project, you will learn:

Neurobiology of different cell types.

Computational modelling and simulation methods.

Modern scientific programming skills, including parallel high-performance computing.

Advanced knowledge of deep learning and AI concepts.

You will become an active member of the Computational Neuroscience and Neuromorphic Engineering (CNET) team at Ulster University, working closely with computational neuroscientists and neurotechnology engineers. Project supervisors Dr Cian O’Donnell and Prof Liam McDaid have substantial experience on biological modelling of synapses and astrocytes (O’Donnell et al, 2014; Rodrigues et al 2021; Wade et al 2011; Flanagan et al, 2021). You will also be part of international collaborations with external supervisor Dr Tom Bartol, computational neuroscientist at the Salk Institute, La Jolla, California; and experimental neuroscientists at University of Bristol, UK.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living