About the Project
Recent weight-loss medications have revolutionised obesity treatment at the population level, but sustaining long-term behavioural change in individual patients remains a challenge. To move beyond broad metabolic interventions, we must uncover the neural circuit dynamics driving feeding behaviour. While current research on feeding circuitry focuses on deep-brain structures like the hypothalamus, the role of "cognitive" regions remains poorly understood. Our recent studies show that the…
prefrontal cortex (PFC) directly influences hypothalamic feeding centres 1,2 , suggesting it establishes the motivational states implemented by the hypothalamus. The PFC also resolves need-state uncertainty (e.g., hunger vs. thirst) and integrates social signals that influence feeding. Because the PFC exhibits complex, heterogeneous activity patterns, computational techniques are required to decode how it coordinates these competing drives. We hypothesise that the PFC integrates these signals to maintain an internal model of ongoing feeding, which can be leveraged to entrain circuit activity toward a long-term satiety state. This project will build computational models of PFC activity during feeding, during weight-loss medication, and will test how perturbing PFC output to the hypothalamus influences the activity patterns within the PFC.
Project Aims
- Decode Latent Dynamics: Using pre-existing datasets in the lab, identify key factors in cortical neuron activity using dimensionality reduction and state-space modelling.
- Investigate Medication Impacts: Using pre-existing datasets in the lab, evaluate changes to latent neural dynamics under anti-obesity medications (e.g., incretin analogues) and following drug cessation.
- Causally Test Internal Model Hypothesis: Using red-shifted calcium imaging and blue shifted optogenetics, block cortical output to the hypothalamus during specific computational states.
Training and Environment
Throughout this interdisciplinary PhD project, the doctoral student will receive comprehensive, dual-track training:
- NeuroAI & Data Science (with Dr. Kudryashova): Computational neuroscience, dynamical systems, information theory, data science, and probabilistic inference for neural populations.
- Systems Neuroscience (with Dr. Karnani): representations and communication between brain areas, analysis of neural code, advanced in vivo methods, including calcium imaging in living rodents, and related data analysis.
Embedded in a vibrant, cross-disciplinary environment, you will develop high-value transferable skills, learn to collaborate across technical boundaries, and communicate complex research to diverse audiences.
Candidate Profile
We seek a highly motivated student with a background in neuroscience, computer science, physics, mathematics, or a related quantitative discipline. Experience in programming (Python/MATLAB) or data analysis is highly desirable.
This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership (DTP). Detailed guidance on the application process, and the EASTBIO Application and Reference Forms can be found at How to Apply | Biology.
Please send your completed EASTBIO Application Form along with a copy of your academic transcripts (as a pdf) to INCR.PostgraduateRecruitment@ed.ac.uk. You should also contact your referees and ask them to submit their references (on the EASTBIO reference form template) to INCR.PostgraduateRecruitment@ed.ac.uk by the application deadline of 16th November 2026.
Funding Notes
This opportunity is open to UK and international students and provides funding covering stipend and UK level tuition fees. The University of Edinburgh covers the difference between home and international fees meaning that the EASTBIO DTP offers fully-funded studentships to all appointees. There is a cap on the number of international students the DTP recruits. It is therefore important for us to know from the outset which fees status category applicants will fall under when applying to our University.
Please refer to UKRI website and Annex B of the UKRI Training Grant Terms and Conditions for full eligibility criteria.
References
- Hartmann, C. et al. 10.24072/pcjournal.416 (2024)
- Razenberg, L. J. A. M. et al. 10.1113/JP288596 (2026)
