PhD Study : Emergent computation of decision uncertainty monitoring, awareness and learning: A spiking neural network modelling approach

Summary

It is well known that some animals, particularly humans, have a sense of self-awareness. Awareness can be reported or inferred when undergoing various forms of cognitive tasks such as when making decisions. Highly studied tasks in which such metacognition is present include perceptual decision-making, which involves the transformation of sensory perception into a single decision, typically requiring some form of evidence accumulation over time [1]. Our previous computational modelling work has developed a biologically based neural network model for monitoring decision uncertainty level on-the-fly [2]. Our model could replicate certain key characteristics of decision uncertainty (or its reciprocal, decision confidence), while also accounting for change-of-mind behaviour via internal feedback control mechanism [2, 3].

This PhD project aims to understand how the decision uncertainty monitoring computation can emerge from recurrently connected spiking neural network model [4], and whether simpler versions of the model can offer mechanistic explanation. Network behavioural outcomes will be compared with data from various experiments from collaborators and open data. As many clinical and cognitive conditions can be linked to awareness issues, the model may shed light on their neural circuit mechanisms. The model will be further extended for machine self-awareness and novel internally generated brain-inspired learning algorithms for AI applications (e.g. [4, 5]). 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 the Magee Campus.

The successful PhD candidate will benefit from the expertise of Ulster University’s Computational Neuroscience, AI, Machine Learning and Computational Biology communities, and will interact closely with various leading international collaborators. The student will gain valuable knowledge in data mining and machine learning techniques, computational modelling, high-performance computing, applications of mathematics/statistics, and the brain sciences. These are essential in many areas of science, engineering, mathematics, and health and biomedical sciences. This training will provide wide opportunities for finding skilled work, especially in the burgeoning field of AI and data science/analytics.

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
• 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%
• 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

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 £19,000 (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