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 <https://www.ucl.ac.uk/research/domains/neuroscience/ucl-neuroai>. 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 <https://www.ucl.ac.uk/research/domains/neuroscience/ucl-neuroai>. *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 Sainsbury Wellcome Centre <https://www.sainsburywellcome.org/web/>, the Crick Partner Networking Fund <https://www.crick.ac.uk/research/research-partnerships/university-partnershi...> and AIBIO-UK <https://aibio.ac.uk/>. We look forward to seeing you there.