Dear Colleagues,
The 6th annual UCL NeuroAI Conference will take place on
Wednesday 5 November 2025 at UCL Great Ormond Street Institute of
Child Health in London. The event features leading speakers at the
cutting edge of machine learning and neuroscience. The UCL NeuroAI
initiative aims to be a central hub where researchers working in
neuroscience, AI, machine learning, and related fields can
interact and stay updated on the latest advancements in these
intersecting areas. Our annual conference is an important
opportunity for these communities to gather, make new connections,
and benefit from insights in one another’s fields.
Keynote speakers
We are privileged to have a distinguished line-up of speakers and
experts confirmed to date.
Leena Chennuru Vankadara (Gatsby Computational Neuroscience
Unit, UCL)
Netta Cohen (University of Leeds)
Nathaniel Daw (Princeton University)
Quentin Huys (Institute of Neurology, UCL)
Kenneth Harris (Institute of Neurology, UCL)
Armin Lak (University of Oxford)
All information including how to register can be found here.
Spaces are limited so we recommend booking asap to avoid
disappointment. Registration fee for the conference is £5. If you
are unable to pay for any reason, please contact neuroaievents@ucl.ac.uk.
Poster and lightning talks - Submissions are now open!
This year we will also be accepting abstract submissions for posters
or a short lightning presentation as part of the event. The deadline
to submit your abstract (200 words) is Wednesday 15th October,
12pm.
Further details regarding the poster and lighting talks can be found
here.
About UCL NeuroAI
The last decade has seen phenomenal advances in the fields of
machine learning (e.g. deep learning, reinforcement learning, and
AI). While these changes have already had considerable impact on
most areas of science they hold a particular resonance for
neuroscience.
Crucially, AI shares a common lineage with neuroscience and
fundamentally machine learning and the brain employ similar
computations to process and compress information. For these reasons
AI provides a means to emulate neural functions and the circuits
supporting them, providing insights to aid our understanding of the
brain and cognition.
Equally, AI tools provide a means to discover, segment, and track
distinct neural and behavioural states - yielding more efficient
experiments and accelerating the pace of discovery. In turn, this
understanding feeds back into the design of more effective AI
architectures and models.
Essentially, AI problems posed in neuroscience both require and
inspire further advances in AI.
Sponsorship
We are immensely grateful for the support from the , the and .
We look forward to seeing you there.