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.
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.
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.
This studentship is open to students applying for CONACyT funding. CONACyT will provide a contribution towards your tuition fees each year and Queen Mary will waive the remaining fee. CONACyT will pay a stipend towards living costs to its scholars. Further information can be found here: https://conacyt.mx/convocatorias/convocatorias-becas-al-extranjero/
Please refer to the CONACyT website here: https://conacyt.mx/convocatorias/convocatorias-becas-al-extranjero/ 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 yul.kang@qmul.ac.uk
Applicants will need to complete an online application form to be considered, 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 CONACyT scholarship (as well as any academic conditions still required to meet our entry requirements).
Once applicants have obtained their offer letter from Queen Mary they should then apply to CONACyT for the scholarship as per their requirements and deadlines, with the support of the project supervisor.
Only applicants who are successful in their application to CONACyT can be issued an unconditional offer and enrol on our PhD programme.
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Kang YHR, Wolpert DM, Lengyel M, Spatial uncertainty provides a unifying account of navigation behavior and grid field deformations, Contributed Talk, Bernstein Conference (2021). https://vimeo.com/showcase/8949778/video/612895009
Chen, G., Lu, Y., King, J. A., Cacucci, F. & Burgess, N. Differential influences of environment and self-motion on place and grid cell firing. Nat Commun 10, 630 (2019). https://www.nature.com/articles/s41467-019-08550-1