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.

Gatsby Computational Neuroscience Unit

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 40 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 third successful quinquennial review in 2015, 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 learnin

Arthur Gretton


Machine learning include the design and training of generative models, nonparametric hypothesis testing, and kernel methods.

Peter Latham


Network dynamics and neural coding

Peter Orbanz

Associate Professor of Machine Learning

Mathematical aspects of machine learning and pattern recognition