First preprint from the lab! Using intracellular recordings & analysis of 2-photon imaging data, we show that spiking & neuromodulatory input during experience drive a reorganization of visuomotor inputs in V1 layer 2/3 neurons, consistent with enhanced visuomotor cancellation - bioRxiv link below.
analogous to the role of a bottom-up sensory input - i.e. L5 input and motor-related predictions influence L2/3 PE with opposite signs.
Our data here actually makes no constraints for the BAC firing coincidence detection idea. This might well happen at the level of L5. At the level of L2/3, however, the signs of influence are all just consistent with L5 functioning as a teaching input for L2/3 -
So, the combination of these two wrong signs is exactly why we think cortex follows something like a JEPA algorithm, and not PP.
The sign of interactions between L5 neurons and L2/3 PE neurons is “wrong” with respect to the non-hierarchical model of PP. At the same time the sign of interactions between PE and L5 is also “wrong” but with respect to the hierarchical model.
Since both networks receive the input, the representations are grounded in the input structure. And the asymmetric architecture where only one network’s representations are used for prediction is what empirically prevents collapse. (See e.g. arxiv.org/abs/2212.048...)
See, for instance, openreview.net/pdf?id=BZ5a1... for the description of the idea.
Hi Chris! Having the learning objective entirely in representation space functions to encourage learning of the representations that are semantically rich and task-structured, while reducing the pressure to focus on irrelevant low-level details (e.g. pixel-level features).
We think cortex might function like a JEPA. It looks like prediction errors in layer 2/3 are not computed against input (as is the idea in predictive processing), but against a representation in latent space (i.e. like in a JEPA arxiv.org/abs/2301.08243 or RPL doi.org/10.1101/2025...).
Most importantly, this framework is falsifiable – mapping JEPA architectures onto cortical circuits (see also the RPL proposal bsky.app/profile/fzen...) produces experimental predictions that we can test to further constrain the search space for algorithms of cortical function!
We think the idea of a JEPA could be an opportune starting point for developing the next iteration of a hypothesis for the algorithm of cortical function. It shows consistency with physiological and anatomical data and addresses many of the PP limitations.
This suggested to us that cortex might function similar to a JEPA @yann-lecun.bsky.social, in which deep and superficial cortical layers implement the encoder networks and prediction error is minimized in latent space rather than input space.
We investigated this idea further by developing a paradigm of artificial coupling between L5 activity and motor-related predictions in the absence of sensory input. We discovered that a mismatch between L5 activity and motor prediction is indeed sufficient to drive prediction error responses in L2/3
However, we discovered that L5 functionally interacts with L2/3 like a bottom-up teaching signal, and we hypothesized that L2/3 might function to model and predict L5 activity rather than raw sensory input.
By probing the functional influence between neurons of deep and superficial cortical layers, we were able to test the experimental predictions the two implementation proposals make. Intriguingly, neither proposal could account for the interlaminar interactions that we observed!
Most prevalent circuit models of PP postulate laminar segregation between prediction error neurons and internal representation neurons. We set out to experimentally distinguish between two PP proposals that differ in their assumptions on whether the cortex is a hierarchy.
Our work with @georgkeller.bsky.social on testing predictive processing (PP) models in cortex is out on biorvix now! www.biorxiv.org/content/10.6... A short thread on our findings and thoughts on where we should move on from PP below.
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1/n: A new collaborative preprint from the lab to start the year: "A multi-ring shifter network computes head direction in zebrafish" together with Siyuan Mei, Martin Stemmler and Andreas Herz from the LMU, Munich.
1/6 New preprint 🚀 How does the cortex learn to represent things and how they move without reconstructing sensory stimuli? We developed a circuit-centric recurrent predictive learning (RPL) model based on JEPAs.
🔗 doi.org/10.1101/2025...
Led by @atenagm.bsky.social @mshalvagal.bsky.social
One of the most promising approaches to making headway in understanding the cortical algorithm that I have seen in a long time! www.biorxiv.org/content/10.1...
Join us for the second Neurobiology of Mental Health conference (May 2026) that will explore the biological mechanisms underlying mental health challenges and their treatment. Information and application on: lakeconferences.org. The deadline for applications is January 31st, 2026.
How does the brain balance learning new things without overwriting what it already knows? Our new paper tackles this long-standing stability–plasticity dilemma during active navigation. With Tony Drinnenberg from the Deisseroth Lab (@deisseroth.bsky.social)
doi.org/10.1101/2025...
I’m super excited to finally put my recent work with @behrenstimb.bsky.social on bioRxiv, where we develop a new mechanistic theory of how PFC structures adaptive behaviour using attractor dynamics in space and time!
www.biorxiv.org/content/10.1...
This year's Ruth Chiquet Prize goes to @solygamagda.bsky.social, who sent a video message, for her work on how the brain detects sensory mismatches. Read more at: www.fmi.ch/news-events/...
A new preprint from our lab with @zelechowski.bsky.social & @georgkeller.bsky.social !
Using wireless EEG + VR, we recorded visuomotor mismatch responses in freely moving humans.
Huge thanks to all participants, Keller Lab members and FMI facilities!
Read more: www.biorxiv.org/content/10.1...
1/N What are the organizational principles underlying crossmodal cortical connections?
We address this in this new preprint, led by @alexegeaweiss.bsky.social & @bturner-bridger.bsky.social in collab w/ @petrznam.bsky.social @crick.ac.uk
www.biorxiv.org/content/10.1...
Ditching months-long delays for fast, constructive feedback.
This interview with @solygamagda.bsky.social dives into the experience of publishing with eLife and what it could mean for a more open and efficient future in science.
100%, but also - the eLife experiment is a good reminer that we need experiments here. Open reviews were not a thing until they were, peer reviews were not a thing until they were, so did preprints, and the ability not to respond to reviewers comments etc. We can try to re-imagine publishing!
Let's say you have a journal that isn't worried about protecting an impact factor, so it didn't need to package a million results into a single paper (to maximise citation-to-publication ratio). What would you do? Couple of suggestions below. Others very much appreciated!
