Cognitive Computational Neuroscience: Emergent decision and related computation with biological recurrent neural networks

With the advent of technology, data in brain sciences are becoming more complex. To understand brain functions and their underlying biological mechanisms, advanced data science and biologically plausible computational modelling are needed. There is increasing application of recurrent neural network models in understanding the neural basis of cognition such as decision-making.

This project aims to develop and apply data science methods and biologically plausible recurrent neural network models to understand decision-making and related cognition, as emergent computations. As machine learning algorithms inherently perform decision-making, the developed neural model(s) will in turn inspire the development of advanced machine learning algorithms.

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 Ph.D. candidate will benefit from the expertise of Ulster University’s Computational Neuroscience, Neurotechnology, 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. This training will provide wide opportunities for finding skilled work in academia or industry, especially in the burgeoning field of neuroscience, AI and data science/analytics. In 2021, Ulster University was ranked 2nd in the UK for Ph.D. researcher satisfaction, 6th largest Computer Science and Informatics unit, and 7th for the level of world-leading or internationally excellent research and impact with respect to staff number.

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

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

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

The University offers the following levels of support:

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.

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

[1] Zador et al. (2023) Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications, 14(1):1597. doi: 10.1038/s41467-023-37180-x.

[2] O’Connell, Shadlen, Wong-Lin and Kelly (2018) Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences, 41(11):838-852.

[3] Atiya, Rañó, Prasad and Wong-Lin (2019) A neural circuit model of decision uncertainty and change-of-mind. Nature Communications, 10(1):2287. doi: 10.1038/s41467-019-10316-8.

[4] Wong and Wang (2006) A recurrent network mechanism of time integration in perceptual decision decisions. The Journal of Neuroscience, 26(4):1314-1328.

[5] Roach, Churchland and Engel (2023) Choice selective inhibition drives stability and competition in decision circuits. Nature Communications, 14(1):147. doi: 10.1038/s41467-023-35822-8.

[6] Lin, Zou, Ji, Huang, Wu and Mi (2021) A brain-inspired computational model for spatio-temporal information processing. Neural Networks, 143:74-87.