PhD Study : Computational Modelling and Machine Learning in Decision Neuroscience

Summary

Decision making is a hallmark of intelligence and a basic building block for cognition that is essential to almost all our mental activities. Consequently, its disruption is central to the cognitive impairments associated with a wide variety of brain disorders. Hence, delineating the key neural mechanisms and computations through which decisions are formed are central to our understanding of intelligence and diagnosis of cognitive deficits. However, the key mechanisms the brain utilises for making abstract decisions are still unclear.

The aim of this 3-year PhD project is to develop computational techniques and models to understand brain and behavioural data across primate species. Specifically, the project will apply techniques in computational neuroscience, particularly biologically based neural network modelling, to elucidate the mechanisms underlying perceptual decisions abstracted from movements. The project will also apply machine learning methods to analyse various types of neural and behavioural data.

This PhD project is part of an ambitious externally funded 5-year research project proposes to break new ground by integrating data from multimodal human and non-human primate neurophysiology with computational modelling to gain convergent insights into how and where abstract decision mechanisms take place in the human and monkey brain, and provide an unprecedented, detailed view on the brain’s decision making machinery.

This project is a collaboration among leading researchers at Columbia University (New York, USA), Northwell-Hofstra School of Medicine (New York, USA), Trinity College Dublin (Dublin, Republic of Ireland), University College Dublin (Dublin, Republic of Ireland), and Ulster University (Derry~Londonderry, Northern Ireland).

This timely and exciting project is available in the Computer Science Research Institute and is tenable in the Faculty of Computing, Engineering and the Built Environment at Magee Campus. The successful PhD candidate will benefit from the expertise of Ulster’s Cognitive and Computational Neuroscience, Machine Learning, and Computational Biology communities, and will interact closely with international experimental collaborators. The student will gain valuable skills and knowledge in computational and mathematical modelling, biological signal processing, machine learning, high-performance computing, mathematics/statistics, and brain sciences.

With research funding from the First/Chair Supervisor, the student will also receive additional financial support e.g. for additional specialized training, attending research meetings and conferences, and computational resources.

Relevant references:

(i) O'Connell RG, Shadlen MN, Wong-Lin K, Kelly SP. Bridging Neural and Computational Viewpoints  on Perceptual Decision-Making. Trends Neurosci. 2018;41(11):838‐852. doi:10.1016/j.tins.2018.06.005

(ii) Atiya NAA, Rañó I, Prasad G, Wong-Lin K. A neural circuit model of decision uncertainty and change-of-mind. Nat Commun. 2019;10(1):2287. doi:10.1038/s41467-019-10316-8

(iii) O'Connell RG, Dockree PM, Kelly SP. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat Neurosci. 2012;15(12):1729‐1735. doi:10.1038/nn.3248

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.

• Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
• 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%
• Publications - peer-reviewed
• Experience of presentation of research findings
• Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding and eligibility

This project is funded by:

• DfE MATCH

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. 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.