Irina Barnaveli

Super thrilled to finally share the results of our team effort, from the lab of the one and only @ayab.bsky.social. Hopefully it will stir interesting discussions about how the brain balances continuity and segmentation

How do we achieve few-shot generalization? New work led by @fabianrenz.bsky.social dives into the role of replay in learning and using structure to generalize reward. Dream team effort with Shany Grossman @nathanieldaw.bsky.social Peter Dayan & @doellerlab.bsky.social
www.biorxiv.org/content/10.6...

Efficient coding in working memory is adapted to the structure of the environment Huang et al. show that the brain optimizes working memory by compressing information when environmental regularities exist. MEG reveals distinct neural systems for abstract structure and item details,...

Our new paper, now published in Cell Reports, asks how the brain adaptively shapes its representations according to the statistical structure of the environment to overcome the limits of working memory capacity.

www.cell.com/cell-reports...

New preprint: Inference over hidden contexts shapes the geometry of conceptual knowledge for flexible behaviour.

In this pre-reg study, our core claim was that we don’t just learn stimulus-reward. We infer hidden context and that inference re-wires attention and neural state space on the fly.
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Happy to share this early Christmas present 🎄: our paper about geometry- and locomotion-dependence of 3D memory got published in PNAS! Joint work with co-first-author Volker Reisner (@reisnerv.bsky.social) as well as Leonard König, Misun Kim & Christian Doeller
www.pnas.org/doi/10.1073/

Probabilistic Foundations of Fuzzy Simplicial Sets for Nonlinear Dimensionality Reduction Fuzzy simplicial sets have become an object of interest in dimensionality reduction and manifold learning, most prominently through their role in UMAP. However, their definition through tools from alg...

New preprint! Have you ever wondered, what are these fuzzy simplicial sets, the theoretical framework behind e.g. UMAP? Here we show that you may simply see them as marginal distributions over simplicial sets. This provides a generative model for UMAP. (1/2)

arxiv.org/abs/2512.03899

The effects of task similarity during representation learning in brains and neural networks Nature Communications - Here, the authors show learning tasks with similar structures can initially cause interference and slow down learning, but both the brain and artificial networks gradually...

Our new paper, now published in @natcomms.nature.com , asks a simple question: when two tasks share a common structure, does the brain learn them more efficiently? Surprisingly, this was not the case. Thread below (1/7)
rdcu.be/eSwvU

Dynamic updating of cognitive maps via traces of experience in the subiculum In the classical view of hippocampal function, the subiculum is assigned the role as the output layer. In spatial paradigms, some subiculum neurons manifest as so-called boundary vector cells (BVCs), ...

New preprint from the lab and great work by Fei Wang. We show how subiculum trace vector cells can be modeled consistent with known effects in CA1. Traces are driven by a mismatch learning rule to keep associative memories in line with experience.
www.biorxiv.org/content/10.1...

Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact dur...

🚨 New preprint! Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference 🧠

We compare humans and neural networks in a learning task, showing how training regime and task similarity interact to drive transfer or interference.

www.biorxiv.org/content/10.1...

Sofie Valk and Daniel Reznik receive ERC Starting Grants Sofie Valk and Daniel Reznik receive ERC Starting Grants

Huge congratulations 🥳 to
@sofievalk.bsky.social
&
@reznikdan.bsky.social
for receiving ERC Starting Grants 2025! We're proud of you!
Learn more about their projects: www.cbs.mpg.de/2397348/2025...

New theory: Categorization is Baked Into the Brain
Categorization is not a late stage of sensory processing but a core computation that begins at the earliest stages, occurring throughout the cortex as predictive feedback shapes feedforward processing.
doi.org/10.31234/osf...
#neuroscience

Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units Neural networks now generate text, images, and speech with billions of parameters, producing a need to know how each neural unit contributes to these high-dimensional outputs. Existing explainable-AI ...

🚨Preprint Alert: Who Does What in Deep Learning? Multidimensional Contribution of Neural Units using Game Theory: arxiv.org/abs/2506.19732
The result of my MSc thesis is out with @kayson.bsky.social @fatemehhadaeghi.bsky.social @patrickmineault.bsky.social @kordinglab.bsky.social, Claus C. Hilgetag

Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation Author summary The hippocampus is a brain region which plays a crucial role in spatial navigation for both animals and humans. Contemporarily, it’s thought to store predictive representations of the e...

Excited to share that our work on the interplay of symmetric learning rules and successor representations (SR) is published in @plos.org (comp.bio)

Work done together with @doellerlab.bsky.social, @caswell.bsky.social and Juergen Jost.

doi.org/10.1371/jour...
#neuroskyence #compneurosky

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📣 Very excited for our symposium on “Building Knowledge Structures” tomorrow at 16:30 at @pug2025.bsky.social

Together with amazing people:
@barnaveliirina.bsky.social
@lukaskunz.bsky.social @mirkothm.bsky.social
and Andrea Greve

Re-enacting steps supports human path integration consistent with motor-corrected grid cell drift Efficient navigation, especially in the absence of vision, requires path integration - the continuous updating of spatial position from self-motion cues. However, path integration is prone to cumulati...

New preprint! We explored how learned movement patterns affect our sense of traveled distance and proposed a neurocomputational model that leverages embodied memories to denoise spatial codes.

🔗 www.biorxiv.org/cgi/content/...

Thank you for your interest in our work!

Delayed emergence of EEG-based task-relevant representations This paper examines the effect of a period of quiet wakefulness (an “offline wake” state) on the performance of a decision making task. An initial fee…

🧠 Can a short period of awake, quiet rest help you generalize better? In our new study, we tested this with a learning paradigm comparing offline vs online wake period.
www.sciencedirect.com/science/arti...

Map-like representations might therefore represent how humans interact with their environment in a very general sense. By supporting action selection, cognitive maps could contribute to optimizing the acquisition and exploitation of a wide repertoire of action plans.

Fourth, the hippocampal-entorhinal map-like representation of action-outcome relations interacts with individual action representations in SMA during the action comparison process.

Third, we show the existence of representations in the SMA that are primarily related to the motor implementation of actions and that exist in parallel to the map-like representation of action outcomes in the hippocampal-entorhinal system.

Second, we found that the hippocampal activity scales with the distance between different action combinations in this abstract map that integrates action-outcome associations.

We first show that participants developed an integrated representation of multiple action-outcome associations as an abstract map, the relational structure of which is reflected in a hexadirectional modulation of EC activity.

In our VR tasks, participants learned arbitrary motor action-outcome associations and later compared action combinations while their brain activity was monitored with fMRI.
The outcomes of the discrete and arbitrary motor actions were modulated along the two task-relevant dimensions.

Our paper in @natcomms.nature.com, we show how cognitive maps in the hippocampal system could solve the general problem of representing and relating multiple alternative action plans. 
doi.org/10.1038/s414...

With @doellerlab.bsky.social, Patrick Haggard, @vigano.bsky.social, Daniel Reznik

Cognitive maps could thus contribute to optimizing the acquisition and exploitation of a wide repertoire of action plans

Fourth, the hippocampal-entorhinal map-like representation of action-outcome relations interacts with individual action representations in SMA during the action comparison process.

Third, we show the existence of representations in the SMA that are primarily related to the motor implementation of actions and that exist in parallel to the map-like representation of action outcomes in the hippocampal-entorhinal system.

Second, we found that the hippocampal activity scales with the distance between different action combinations in this abstract map that integrates action-outcome associations.

We first show that participants developed an integrated representation of multiple action-outcome associations as an abstract map, the relational structure of which is reflected in a hexadirectional modulation of EC activity.

In our VR tasks, participants learned arbitrary motor action-outcome associations and later compared action combinations while their brain activity was monitored with fMRI.

The outcomes of the discrete and arbitrary motor actions were modulated along the two task-relevant dimensions.