UCL NeuroAI Talk Series

About 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. The seminars are held online and the Zoom link will be shared 24/48 hours prior to the seminar.

NeuroAI Talk Series

Wednesday 28 January 2026

Time: 2pm - 3pm GMT, via Zoom

Speaker: Kristopher Jensen, Senior Research Fellow, Sainsbury Wellcome Centre, UCL.

Talk Title: A mechanistic theory of planning in prefrontal cortex.

Abstract: Planning is critical for adaptive behaviour in a changing world, because it lets us anticipate the future and adjust our actions accordingly. While prefrontal cortex is crucial for this process, it remains unknown how planning is implemented in neural circuits. Prefrontal representations were recently discovered in simpler sequence memory tasks, where different populations of neurons represent different future time points.

We demonstrate that combining such representations with the ubiquitous principle of neural attractor dynamics allows circuits to solve much richer problems including planning. This is achieved by embedding the environment structure directly in synaptic connections to implement an attractor network that infers desirable futures.

The resulting ‘spacetime attractor’ excels at planning in challenging tasks known to depend on prefrontal cortex. Recurrent neural networks trained by gradient descent on such tasks learn a solution that precisely recapitulates the spacetime attractor – in representation, in dynamics, and in connectivity. Analyses of networks trained across different environment structures reveal a generalisation mechanism that rapidly reconfigures the world model used for planning, without the need for synaptic plasticity. The spacetime attractor is a testable mechanistic theory of planning. If true, it would provide a path towards detailed mechanistic understanding of how prefrontal cortex structures adaptive behaviour.

UPCOMING SEMINARS

Wednesday 18 February - Zac Fountas, Huawei and UCL Queen Square Institute of Neurology.

Wednesday 15 April - Noémi Éltető, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

Sign up to the UCL NeuroAI Newsletter to receive updates on the latest NeuroAI events and opportunities.

Previous speakers:

Please visit the UCL NeuroAI website to access recordings of previous talks.