NeuroAI Conference
Presented by UCL VPEE
The 7th annual UCL NeuroAI Conference will feature 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.
We are delighted to confirm the following keynote speakers for this year’s conference:
Wolfgang Maass, Technische Universität Graz
Agostina Palmigiano, Gatsby Computational Neuroscience Unit
Ellie Pavlik, Brown University
Bernardo Sabatini, Harvard Medical School
Nicolas Skatchkovsky, Francis Crick Institute
Eleni Vasilaki, University of Sheffield
We are immensely grateful for the support from the Sainsbury Wellcome Centre, Callosum Technologies and DeepMind.
Poster and lightning talks
This year we will also be accepting abstract submissions for posters and short lightning presentations (3-4 mins), as well as longer submitted talks (15 mins) as part of the event.
The deadline to submit your abstract for these is Friday 16 October at 12pm.
If you would like to present you should book a place to attend the conference, and then submit your proposal on the form below.
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. UCL NeuroAI has been set up to provide a forum where researchers in these areas can meet, exchange ideas, and move both fields forward.