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Anna Vasilevskaya

@loghyr.bsky.social

PhD student studying cortical computations in https://www.apredictiveprocessinglab.org

15 Posts115 Follower155 Folgt
Helen Barron@helencbarron.bsky.social · last wk.

***New preprint from the lab*** showing “Generative replay across hippocampal-neocortical circuits”. In sleep we show spiking sequences in V1 (sensory neocortex!) that represent information that has not been directly experienced www.biorxiv.org/content/10.6... @bndu.ox.ac.uk @ndcnoxford.bsky.social

Generative replay across hippocampal-neocortical circuits

To make flexible decisions, the brain needs to make inferences between events that were not directly experienced together. In sharp-wave ripple (SWR) events, during periods of rest and sleep, spiking ...

biorxiv.org

Reza Shadmehr@rezashadmehr.bsky.social · last wk.

If an action results in error, each neuron requires an individualized teaching signal that guides change in its output. This is the credit assignment problem of learning. Are there neurons in the brain that can compute such a sophisticated teaching signal? Yes. www.biorxiv.org/content/10.6...

Climbing fibers encode the gradient of a loss function for the cerebellum

Neurons in the brain are often many synapses away from motoneurons, yet if a movement results in error, each distant neuron needs a teacher that considers its specific contribution to production of th...

biorxiv.org

Thomas Akam@thomasakam.bsky.social · 2mo ago

Why does dopamine ramp up during approach to predictable rewards? In this preprint with Luke Priestley, we explore the idea that dopamine ramps occur when reward predictions inferred using a world-model are used to train striatal cached values.

Dopamine ramps as a normative consequence of dual-process control

Midbrain dopamine neurons are thought to implement a temporal difference (TD) reward prediction error (RPE) that updates cached values stored in striatum. This has been challenged by evidence that dop...

biorxiv.org

Eduardo Maristany de las Casas@maristanye.bsky.social · 3mo ago

1/10 When the going gets tough, the dendrites get going. Here is a summary on our new paper just published in @science.org on flexible learning in frontal motor cortex. With @mattlark.bsky.social 🔬. #neuroskyence #neuroscience 🔗 doi.org/10.1126/scie...

Tuft dendrites in frontal motor cortex enable flexible learning

Flexible learning relies on integrating sensory and contextual information to adjust behavioral output in different environments. The anterolateral motor cortex (ALM) is a frontal area critical for ac...

doi.org

Rebecca Jordan@beckyjordan.bsky.social · 5mo ago

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.

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Georg Keller@georgkeller.bsky.social · 6mo ago

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...).

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Archi...

arxiv.org

Anna Vasilevskaya@loghyr.bsky.social · 6mo ago

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.

Anna Vasilevskaya@loghyr.bsky.social · 6mo ago

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.

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

biorxiv.org

Portugues Lab@portugueslab.bsky.social · 7mo ago

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.

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Georg Keller@georgkeller.bsky.social · 8mo ago

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...

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...

biorxiv.org

Alex@alexattinger.bsky.social · 10mo ago

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...

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,...

doi.org

Victor Tatarskiy@greenrat.bsky.social · last yr.

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!

Tim Behrens@behrenstimb.bsky.social · last yr.

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!

Julian Rossbroich@jrbch.bsky.social · last yr.

I've spent much of my PhD thinking about E/I balance, and our latest preprint represents the culmination of that journey. Huge thanks to @fzenke.bsky.social for guiding me. Looking forward to your thoughts & comments.

Friedemann Zenke@fzenke.bsky.social · last yr.

1/6 Why does the brain maintain such precise excitatory-inhibitory balance? Our new preprint explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signals for learning. www.biorxiv.org/content/10.1... led by @jrbch.bsky.social

Antonio Falasconi@antofala.bsky.social · last yr.

Forelimb movement control at the basal ganglia - brainstem interface! Happy to finally share this work from me and @harsh-kanodia.bsky.social with Silvia Arber! @biozentrum.unibas.ch @fmiscience.bsky.social www.nature.com/articles/s41...

Dynamic basal ganglia output signals license and suppress forelimb movements - Nature

Basal ganglia output neurons fire dynamically in bidirectional and movement-specific patterns to license forelimb movements. 

nature.com

Markus Meister@mameister4.bsky.social · last yr.

If you're planning a course with mathematical methods content, or use such methods in your own work, please take a look at "Mathematics in Biology", by Meister, Lee, and Portugues, published at MIT Press. 1/2 @portugueslab.bsky.social mitpress.mit.edu/978026204940...

Mathematics in Biology

Biology has turned into a quantitative science. The core problems in the life sciences today involve complex systems that require mathematical expression, ye...

mitpress.mit.edu