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Whatโ€™s especially interesting is going beyond the final output variable, e.g., days of the week: asking what latent variables the model uses internally, how they are represented geometrically, and whether manifold-aware steering can causally test how they give rise to downstream behavior. (2/3)
Beyond that, the bigger challenge is unsupervised discovery of those latent manifolds from activations + behavior: discovering the underlying representations that drive cognitive behavior without assuming labels for those latents in advance. (3/3)
Really enjoyed Goodfireโ€™s neural geometry posts. A few thoughts. The current examples mostly focus on explicit variables, e.g., days of the week, and show how their geometry in activation space mirrors the geometry of the modelโ€™s outputs/behavior. (1/3)
๐Ÿš€ PhD position in #NeuroAI & neurodevelopment ๐Ÿš€ Co-supervised by Sarah Lippรฉ and myself, to investigate visual processing & cognition abnormalities in children with neurodevelopmental disorders in a neuroAI framework. Full project details and how to apply here: tinyurl.com/kbuyntpn ๐Ÿง ๐Ÿค– ๐Ÿ“ˆ
Want to match neural representations from different days and get more trials for analyses? Interested in multi-scale neural dynamics in decision variability? Visit our #cosyne2026 poster today afternoon (Sat)! 3-161: Dynamics-based alignment across sessions reveals latent neural computation
We've updated the preprint of our Naturalistic Computational Cognitive Science paper (arxiv.org/abs/2502.20349) โ€” we've tried to clarify and streamline the arguments, and added some new examples: 1/5
Don't miss @maxschwabe.bsky.social's poster this evening (our master's student and presenter awardee ๐ŸŽ‰) on decomposing RNN dynamics during naturalistic decision-making! It was a joint work with @armanbehrad.bsky.social and @roxana-zeraati.bsky.social!
New paper ๐Ÿšจ #ICLR26 Most world models predict the future from a past trajectory. But neuroscience suggests that such inference can instead be made from temporally independent experiences. We built the Episodic Spatial World Model (ESWM), a model that does exactly this: Video abstract [1/2]