PhD Study : NeuroAI approaches to understanding inter-individual differences in cognition and psychiatric disorders

Summary

An important unsolved problem in psychology and neuroscience is: what determines the cognitive differences between individual people? And relatedly, why do some people develop psychiatric disorders while others don’t? One clue may come from the fact that human brain wiring is partially specified by our genomes. Modern genetics has found that a large fraction of both inter-individual cognitive differences and psychiatric disorder risk is due to inter-individual variations in our DNA: for example autism is believed to be ~80% genetic, while schizophrenia is ~50% genetic [Mitchell 2018].

Understanding how genes affect cognition requires understanding how the genome sets the rules for brain wiring during early development, which, in combination with a person’s life experience, results in their particular cognitive profile.

In this project, the PhD student will build and study biologically-plausible deep neural networks as simplified models human brains [Doerig et al 2023]. Standard artificial neural networks will be adapted with biological components such as neuronal cell types, astrocytes, and spiking dynamics. This is an example of the growing field of “NeuroAI”, at the interface between computational neuroscience and artificial intelligence [Zador et al 2023].

The project will have three phases:

1. Design a basic neural network model and   genome-to-network architecture mapping. Identify the factors that determine   the effect of inter-individual genetic differences on network task   performance (nature vs nurture).
2. Extend the model for the random   distribution of genomes using principles from evolutionary theory   [Eyre-Walker 2010]. For example, genetic mixing via sexual reproduction in   the population could be modelled to ask if the DNNs can account for human   sibling similarity in cognitive traits.
3. Explore how stable subpopulations of  individuals with psychiatric disorders could arise in the model.

The results of these simulation studies can be used to make predictions for neuroscience experiments, or inspire new principles for designing next-generation AI systems.

The student will be embedded in the CNET team in the Intelligent Systems Research Centre at Ulster University, and will collaborate with Dr Kevin Mitchell at Trinity College Dublin.

The student will acquire skills in:

• deep neural networks
• scientific computer programming
• analysis of neuroscience and clinical data
• computational modelling of biological systems

Please note that a research proposal is NOT required for this project.

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
• A demonstrable interest in the research area associated with the studentship

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