Neuroscience
Gatsby computational neuroscience unit.
We established this unit in 1998 at University College London (UCL) to provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with UCL’s other world-class research groups in neuroscience and related areas.
The Gatsby Computational Neuroscience Unit (GCNU) was established with the original aim of “building neurobiologically realistic and computationally sound models of the way that the brain computes”.
GCNU’s core strengths are in computationally and probabilistically-oriented theoretical neuroscience, and statistical machine learning. In total there are approximately 50 researchers, students and support staff. GCNU’s teaching activities are centred on an innovative four-year PhD programme in Computational Neuroscience and Machine Learning.
Following its fourth successful quinquennial review in 2021, the Unit is continuing its research and educational programme in these general directions, broadening and deepening its collaborations both within UCL and outside, and strengthening its theoretical bases in machine learning. The Unit strives to be one of the foremost centres in the world overall, and the GCNU model has been duplicated by various institutions.
In mid-2015, GCNU moved into the Sainsbury Wellcome Centre building, taking up a central location that connects with the SWC experimental laboratory space and nearby break-out spaces to facilitate discussions and collaborations bewteen theorists and experimentalists.
Research groups
Maneesh sahani.
Director; Professor of Theoretical Neuroscience and Machine Learning
Understanding how information is represented in neural systems, and how this representation underlies computation and learning
Arthur Gretton
Professor of Machine Learning
Machine learning include the design and training of generative models, nonparametric hypothesis testing, and kernel methods
Peter Latham
Professor of Theoretical Neuroscience
Network dynamics and neural coding
Peter Orbanz
Mathematical aspects of machine learning and pattern recognition
Agostina Palmigiano
Research interests in the i) computational principles underlying sensory/motor processing and learning ii) how behavioural information (as choice, reward) across the cortex shapes area-specific computations, and iii) how interspecies differences in circuit architecture and dynamical regimes limit/facilitate learning
Andrew Saxe
Group Leader in Machine Learning applied to Neuroscience (jointly with the Sainsbury Wellcome Centre)
A mathematical toolkit suitable for analysing and describing learning in the brain and mind
Robot Perception and Learning Lab
PhD Application Instructions
How to apply.
- UCL CDT of Foundational AI: here
- UCL CDT of Cybersecurity: here
- UCL Computer Science: here (Department: Computer Science; Programme: Postgraduate Research; Full-time; Research Degree: Computer Science (4 Year Programme))
Research Proposal
- Transcript of records
- Research proposal (approximately 4-8 pages)
- Description of what excites you
- A lay summary (which can be read by someone, who is not from the field) of your research proposal (up to 200 words)
- Literature overview (what do other people currently do in the area you are interested in?)
- How would you fill in existing gaps?
- A concrete 6-months project you wish to start your PhD with (including timeline)
- Why you would like to join RPL, UCL-CS, and CDT of Foundational AI in particular
- Download template for research proposal: here
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