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Posts by Anna Vasilevskaya

For nearly a century, we believed the therapeutic effect of ECT is the seizure. Our latest research suggests we may have been looking at the wrong event.
A thread on why cortical spreading depression (CSD) might be the driver of therapeutic benefit.
Work led by @therehugolad.bsky.social

6 days ago 38 19 1 1

Very excited to share new work by Leonardo Lupori. In mice, a single dose of the antipsychotic clozapine results in changes to behavior and cortical activity patterns that remain detectable a week later.
www.biorxiv.org/content/10.6...

3 weeks ago 19 8 1 0
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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.

1 month ago 75 23 1 3

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.

1 month ago 1 0 1 0

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 -

1 month ago 1 0 1 0

So, the combination of these two wrong signs is exactly why we think cortex follows something like a JEPA algorithm, and not PP.

1 month ago 0 0 1 0

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.

1 month ago 0 0 1 0
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Implicit variance regularization in non-contrastive SSL Non-contrastive SSL methods like BYOL and SimSiam rely on asymmetric predictor networks to avoid representational collapse without negative samples. Yet, how predictor networks facilitate stable learn...

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...)

1 month ago 1 0 0 0

See, for instance, openreview.net/pdf?id=BZ5a1... for the description of the idea.

1 month ago 0 0 1 0
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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).

1 month ago 1 0 1 0

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...).

2 months ago 45 12 2 2

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!

2 months ago 6 0 2 0

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.

2 months ago 4 0 1 0
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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.

2 months ago 5 0 1 0
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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

2 months ago 3 0 1 0

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.

2 months ago 4 0 1 0
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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!

2 months ago 3 0 1 0

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.

2 months ago 3 0 1 0
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A functional influence based circuit motif that constrains the set of plausible algorithms of cortical function There are several plausible algorithms for cortical function that are specific enough to make testable predictions of the interactions between functionally identified cell types. Many of these algorithms are based on some variant of predictive processing. Here we set out to experimentally distinguish between two such predictive processing variants. A central point of variability between them lies in the proposed vertical communication between layer 2/3 and layer 5, which stems from the diverging assumptions about the computational role of layer 5. One assumes a hierarchically organized architecture and proposes that, within a given node of the network, layer 5 conveys unexplained bottom-up input to prediction error neurons of layer 2/3. The other proposes a non-hierarchical architecture in which internal representation neurons of layer 5 provide predictions for the local prediction error neurons of layer 2/3. We show that the functional influence of layer 2/3 cell types on layer 5 is incompatible with the hierarchical variant, while the functional influence of layer 5 cell types on prediction error neurons of layer 2/3 is incompatible with the non-hierarchical variant. Given these data, we can constrain the space of plausible algorithms of cortical function. We propose a model for cortical function based on a combination of a joint embedding predictive architecture (JEPA) and predictive processing that makes experimentally testable predictions. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13 Novartis Foundation, https://ror.org/04f9t1x17 European Research Council, https://ror.org/0472cxd90, 865617

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.

2 months ago 49 16 2 1
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📆 May 17-21 in Thun, Switzerland
🏔️ All career stages welcomed
⏳ Apply by January 31

Learn more and apply: https://bit.ly/4pCHrAH

🧠📈

2 months ago 5 1 0 0
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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.

3 months ago 58 26 1 3
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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

4 months ago 142 42 3 4
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Understanding cortical computation through the lens of joint-embedding predictive architectures Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynami...

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...

4 months ago 23 4 1 1
Home - Lake Conferences

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.

6 months ago 9 6 0 0
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Environmental Novelty Modulates Rapid Cortical Plasticity During Navigation In novel environments, animals quickly learn to navigate, and position-correlated spatial representations rapidly emerge in both the retrosplenial cortex (RSC) and primary visual cortex (V1). However,...

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...

5 months ago 59 15 1 1
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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...

6 months ago 221 86 9 9
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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/...

7 months ago 11 3 0 1
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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...

8 months ago 28 13 0 1
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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...

8 months ago 91 33 1 5
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Publishing with eLife: “the future of science lies in greater transparency” Neuroscientist Magdalena Solyga shares her latest study and her experience publishing with eLife.

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.

9 months ago 15 9 0 0