- Supervisors: Dr. Yul Kang and Dr. Guifen Chen
- Funding: China Scholarship Council (CSC)
- Deadline: 31st January 2023
Research environment
The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2021 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 180 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.
Dr. Yul Kang and Dr. Guifen Chen have a strong research track record, having published first-author papers in journals such as Science, Nature Communications, PNAS, eLife, and Current Biology. They have a broad network of collaborators at Cambridge, UCL, Columbia University, NYU, and beyond. The psychology department is very well equipped with cutting-edge virtual reality setup and computing resources such as High Performance Compute cluster.
Training and development
Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.
In this project, the PhD student will learn and develop computational methods for research in computational neursocience/cognitive science of spatial navigation, an area that has strong connection with machine learning and robotics. The student will also analyse neural recording performed in a virtual reality environment, and will have an opportunity to build such an environment for behavioural experiments.
Project description
Spatial navigation requires combining multiple sources of information considering their uncertainty. For example, if we turn at a tenth crossroad to visit a particular supermarket only to discover an unexpected signboard, we should think we are at a wrong crossroad if we are unsure how many crossroads we have passed (uncertainty in path integration). However, if we are unsure about the supermarket’s name, we should think we remembered the name incorrectly (uncertainty in landmark recognition).
To perform such arbitration, the brain must track uncertainty about different sources of information on the animal’s current location. However, while it is well known that neurons in the hippocampus and neighbouring areas (place and grid cells) respond to the location of human and nonhuman animals in an environment, it is largely unknown how they respond to uncertainty in the location, and how it relates to the animal’s behavioural performance in navigation.
Finding out how the biological brain handles uncertainty has implications for both medicine and artificial intelligence. For medicine, it can help early diagnosis and inform finding the therapeutic target of Alzheimer’s disease, because the hippocampus and neighbouring areas are the first brain areas to show pathology in Alzheimer’s disease. For artificial intelligence, it can inform the design of artificial neural networks that need to handle uncertainty in a complex real-life task such as spatial navigation.
In this project, we aim to study how different sources of uncertainty differentially affect the location coding in brain areas.
(1) We will extend the primary supervisor (YK)’s computational model that provided a unifying explanation of distortion of neural activity and behavioural response pattern in spatial navigation, to account for moment-by-moment dependence of neural activity on evolving spatial uncertainty (e.g., after seeing an unexpected signboard).
(2) We will compare the model’s prediction with the existing data from the secondary supervisor (GC)’s electrophysiological recordings from place and grid cells from rodents navigating in a virtual reality where the path-integration and landmark uncertainty has been manipulated.
(3) We will build a human version of the virtual reality task and assess the impact of different sources of uncertainty on navigation performance.
Eligibility and applying
Applicants must be:
- Chinese students with a strong academic background.
- Students holding a PR Chinese passport.
- Either be resident in China at the time of application or studying overseas.
- Students with prior experience of studying overseas (including in the UK) are eligible to apply. Chinese QMUL graduates/Masters’ students are eligible for the scheme.
Please refer to the CSC website for full details on eligibility and conditions on the scholarship.
Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree and/or a masters degree in computer science, mathematics, engineering, physics, computational neuroscience/cognitive science, or related disciplines.
An ideal candidate would have strong programming skills (especially in Python) and quantitative skills (especially in machine learning), and passion to find out how the brain works. Previous research and/or industry experience is a plus. Please explain how you meet these criteria in your CV and personal statement.
Applicants from outside of the UK are required to provide evidence of their English language ability. Please see our English language requirements page for details: https://www.qmul.ac.uk/international-students/englishlanguagerequirements/postgraduateresearch/
Informal enquiries about the project can be sent to Dr. Yul Kang at [Email Address Removed]. Formal applications must be submitted through our online form by 31st January 2023 for consideration, including a CV, personal statement and qualifications.
Shortlisted applicants will be invited for a formal interview by the project supervisor. Those who are successful in their application for our PhD programme will be issued with an offer letter which is conditional on securing a CSC scholarship along with academic conditions still required to meet our entry requirements.
Once applicants have obtained their offer letter from Queen Mary they should then apply to CSC for the scholarship by the advertised deadline with the support of the project supervisor.
For September 2023 entry, applicants must complete the CSC application on the CSC website between 10th March - 31st March 2023. Only applicants who are successful in their application to CSC can be issued an unconditional offer and enrol on our PhD programme.
For further information, please go to: https://www.qmul.ac.uk/scholarships/items/china-scholarship-council-scholarships.html
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