The Gatsby Computational Neuroscience Unit shares a purpose-built building with the Sainsbury Wellcome Centre for Neural Circuits and has close research links with colleagues at the SWC.
The Sainsbury Wellcome Centre aims to understand how computation in neural circuits gives rise to flexible, complex behaviour. It is jointly funded by the Gatsby Charitable Foundation and the Wellcome Trust.
The brain is remarkable in its ability to produce a rich array of inbuilt and learned behaviours, which are deployed flexibly to meet individuals’ needs and environmental demands. Behaviour emerges from distributed computations in specialised neural circuits across brain regions, with the same regions often contributing to multiple behaviours. How networks of neurons give rise to this adaptive and complex repertoire of function is a fundamental scientific question of our era.
Research at the Centre seeks to identify a set of elemental neural computations that underlie different behaviours and determine how they are implemented at the level of circuits, cells and synapses, with the long-term goal to use this knowledge to build a coherent theoretical framework that combines these neural computations and explains how complex behavioural processes relate to neural circuit mechanisms.
Linking theoretical and experimental research
Experimental research is producing more and more data as recording techniques become more advanced. This vast amount of data will require sophisticated data analysis tools that can pick up meaningful signals from a lot of noise. Equally, analysis of existing datasets can also generate new questions which can be experimentally tested. One of our future goals is to bridge the gap between theoretical and experimental neuroscience. With this in mind, the GCNU has been working closely with SWC colleagues to develop ways in which we can collaborate and interact, and move neuroscience forward.
We created a joint PhD programme that incorporates taught courses on Systems and Theoretical Neuroscience, Experimental Neuroscience and Machine Learning in order to train a new generation of neuroscientists.
We designed a joint postdoctoral fellowship for early career researchers who are interested both in experimental and theoretical work and who are seeking to build independence.
We developed a new joint faculty position for Machine Learning applied to Neuroscience, with the ambition to apply theoretical and machine learning tools to systems neuroscience questions.
In addition to joint programme activities, our faculty members are actively collaborating and engaging with SWC colleagues on current ongoing research.