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Pablo Marcos-Manchón

@jazzmaniatico.bsky.social
72 followers 103 following 21 posts

ML Engineer trying to do neuroscience

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Reposted by Pablo Marcos-Manchón
Marta Silva @martamasilva.bsky.social · 12/08/2026
Check out our new paper! 🧠 Me, @xiongbowu.bsky.social and @fuentemilla.bsky.social review how reactivation at the end of events support memory formation and propose it serves multiple functions from preventing interference to helping preserve temporal continuity! ✨ www.cell.com/trends/cogni...
cell.com
Neural reactivation supports memory formation when events end
Memory routinely transforms continuous experience into discrete episodes that support flexible retrieval. We propose that these structured representations are established via rapid neural reactivation...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 15/07/2026
A pleasure to work on this with @fuentemilla.bsky.social! For more details, here's the original preprint thread 👇 bsky.app/profile/jazz...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 15/07/2026
After nearly a year as a preprint, our work is now published open access in Communications Biology 🎉 Using representational alignment across brains and AI models, we identified two cortical routes for scene perception. www.nature.com/articles/s42... 🧵 (1/2)
nature.com
Shared representations in brains and models reveal a two-route cortical organization during scene perception - Communications Biology
Representational similarity analysis of human brain fMRI during natural scene viewing reveals two cortical routes: a ventromedial pathway for scene context and layout, and a lateral occipitotemporal p...
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Reposted by Pablo Marcos-Manchón
Raj Magesh @raj-magesh.org · 23/05/2026
Very cool: it's amazing that this doesn't rely on paired data! @mickbonner.bsky.social and I recently found that the shared geometry is characterized by a ~universal power-law: doi.org/10.1371/jour..., Fig 2 Now I'm curious if we could have done it without the paired stimuli...
doi.org
Universal scale-free representations in human visual cortex
Author summary The human cerebral cortex is thought to encode sensory information in population activity patterns, but the statistical structure of these population codes has yet to be characterized. ...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 27/05/2026
Mmm very interesting! I need to take a deeper look at your results! In a related paper, we found that brain–vision model alignment across regions also follows power-law scaling (doi.org/10.1038/s420...; Fig. 2E; Suppl. Note 4), which could perhaps reflect this decay across dimensions 🤔
doi.org
Shared representations in brains and models reveal a two-route cortical organization during scene perception - Communications Biology
Representational similarity analysis of human brain fMRI during natural scene viewing reveals two cortical routes: a ventromedial pathway for scene context and layout, and a lateral occipitotemporal p...
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Reposted by Pablo Marcos-Manchón
Juan Linde-Domingo @lindedomingo.bsky.social · 27/05/2026
Are gist-like shifts in memory over time evidence of engram transformation? Really? Or partly a general response bias? At #APS2026 in Barcelona this week, Mattia Delmarco will present our work on object typicality and visual memory. psychologicalscience.confex.com/psychologica...
psychologicalscience.confex.com
Typicality Effects In Visual Memory: Co-Occurring Representational Drift and Global Bias
Episodic memory is reconstructive: recollection combines stored perceptual deta...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 23/05/2026
[...] without requiring each individual stimulus response to match point-by-point. So the assumption is weaker: subjects do not need to have identical responses, but their representational spaces should have similar relational structure.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 23/05/2026
Hyperalignment uses paired responses to shared stimuli to learn the map, effectively optimizing pointwise correspondence across subjects. Here, the rotation is learned from distributional/geometric structure: similar regions of the stimulus distribution should induce similar response geometry, [...]
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 23/05/2026
Yes, absolutely! Hyperalignment is key background for our work :) The claim is compressed a lot in post form. Both results show that rotations can align representations across subjects. As you said, the difference is in the method and in the correspondence assumption.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
Feedback, comments, and discussions are very welcome :) 📜 Paper: arxiv.org/abs/2605.204... ⌨️ Code: github.com/memory-forma...
arxiv.org
Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively: embeddings can be translated across models throu...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
Synchronizing all pairwise rotations into a single shared latent space improves cross-subject translation. Pairwise translations are not isolated solutions: they are mutually compatible with a common coordinate system.
Pairwise rotations are synchronized into a shared latent neural space.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
Main result: simple orthogonal rotations recover accurate instance-level correspondences across subjects. This suggests that independently learned subject spaces are approximately isometric: different brains encode visual information in geometrically compatible spaces.
Heatmaps showing cross-subject brain-to-brain translation performance for all ordered subject pairs. Panels report Mean Rank and R@1, with darker colors indicating better performance.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
Using the Natural Scenes Dataset, we learn subject-specific fMRI embeddings from brain data alone, using repetitions as self-supervision. Without paired samples or model representations, we learn rotations that translate one subject’s embeddings into another's space.
First, subject-specific fMRI responses are encoded into low-dimensional embeddings using reliability weighting, PCA, MCCA, and nonlinear refinement. Second, pairwise rotations translate embeddings between subjects. Third, pairwise rotations are synchronized into a shared latent neural space.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
The Strong Platonic Representation Hypothesis suggests independently learned embeddings may be translated using geometry alone, without shared data. We ask whether this also holds across human brains: do visual-cortex representations form compatible versions of a shared neural geometry?
Diagram showing two subjects’ fMRI responses to visual images (UMAP projection). Each subject’s responses are encoded into a separate neural embedding space, then rotated into a shared neural space where matching colored points align across subjects.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 21/05/2026
Can neural representations from different people be translated into a shared space without paired data? In our preprint, "Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry", with Rishi Jha and @fuentemilla.bsky.social, we test this using fMRI 🧵👇
Marcos-Manchón, P., Jha, R., & Fuentemilla, L. (2026). Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry. arXiv [q-Bio.NC]. Retrieved from http://arxiv.org/abs/2605.20496
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Reposted by Pablo Marcos-Manchón
Insitute of Neurosciences of the University of Barcelona @ubneuro.bsky.social · 02/03/2026
The #UBneuro PhD Welcome Day 2026 was a great success! 🎓✨ New doctoral researchers connected with the community, explored responsible research, and visited the Cognitive Neuroscience Unit. A strong start for the next generation of neuroscientists! 🧠
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Reposted by Pablo Marcos-Manchón
Lluís Fuentemilla @fuentemilla.bsky.social · 19/01/2026
Is optimism socially transmissible? Our new study shows it propagates via social prediction errors. When we "imagine together," simulation discrepancies drive an update in expectations to align with the group. A dynamic resource shaped by social interaction. Link study: osf.io/preprints/ps...
osf.io
OSF
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Reposted by Pablo Marcos-Manchón
Yann Harel @hyruuk.bsky.social · 13/08/2025
This year at #CCN25 we showed the importance of OOD evaluation to adjudicate between brain models. Our results demonstrate these trivial but key facts : - high encoding accuracy ≠ functional convergence - human brain ≠ NES console ≠ 4-layers CNN - videogames are cool w/ @lune-bellec.bsky.social 🙌
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Reposted by Pablo Marcos-Manchón
Andrew Lampinen @lampinen.bsky.social · 05/08/2025
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices
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Reposted by Pablo Marcos-Manchón
Xiongbo Wu @xiongbowu.bsky.social · 30/07/2025
🚨 New preprint alert! Excited to share our latest work on alpha/beta activity, eye movements, and memory. Across 4 experiments combining scalp EEG/iEEG with eye tracking, we show that alpha/beta activity directly reflects eye movements, and only indirectly relates to memory. 👇 Highlights (1/7):
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Reposted by Pablo Marcos-Manchón
Marta Silva @martamasilva.bsky.social · 01/07/2025
🧠 Paper out! We investigated how hippocampal and cortical ripples support memory during movie watching. We found that: 🎬 Hippocampal ripples mark event boundaries 🧩 Cortical ripples predict later recall Ripples may help transform real-life experiences into lasting memories! rdcu.be/eui9l
rdcu.be
Movie-watching evokes ripple-like activity within events and at event boundaries
Nature Communications - The neural processes involved in memory formation for realistic experiences remain poorly understood. Here, the authors found that ripple-like activity in the human...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
In summary, this shared representational geometry provides a powerful framework to study the brain's functional organization and trace how information is routed through the cortex. We welcome your questions! 💻 Code: github.com/memory-forma... (8/8)
github.com
GitHub - memory-formation/convergent-transformations: Convergent transformations of visual representation in brains and models. P. Marcos-Manchón and L. Fuentemilla (Under review)
Convergent transformations of visual representation in brains and models. P. Marcos-Manchón and L. Fuentemilla (Under review) - memory-formation/convergent-transformations
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
Diving deeper into the LOTC hub's social vs non-social component: Alignment across brains along the lateral stream (EVC→LOTC) is present only when viewing social scenes (with people or animals). This supports its proposed role as a specialized "third visual pathway" for social perception. ⬇️ (7/8)
Split-panel brain and graph plots showing inter-subject representational alignment for social vs non-social scenes. The lateral pathway only emerges during social scene perception.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
So what information does each hub actually encode? Using KMCCA, we studied the primary dimension that organizes each hub's information: 👁️ EVC: Low-level visual features 🏞️ Ventral Hub: Scene & object structure 👨‍👩‍👧‍👦 LOTC Hub: Social vs. non-social content ⬇️ (6/8)
Scatter plots of shared representational components in three brain hubs (KMCCA top 2 dimensions). Early visual cortex shows low-level structure; the ventral hub encodes scene layout; LOTC separates social (human, animal) from non-social stimuli.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
Vision DNNs capture this shared geometry, with each brain hub showing a different layer alignment profile: 🧠 Early visual ↔️ Shallow DNN layers 🧠 Ventral hub ↔️ Mixed DNN layers 🧠 LOTC ↔️ Deep DNN layers Language Models only align with the high-level LOTC hub. ⬇️ (5/8)
Comparison of brain alignment with deep vision (left) and language models (right). Vision models align broadly across cortex, with early areas matching shallow layers and higher areas matching deeper layers. Language models only align with LOTC. Line plots show RSA scores across model depth for three brain hubs.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
This shared representational geometry is so consistent across people that we could map a whole-brain connectivity network based on it, revealing interactions between visual, memory and prefrontal areas. ⬇️ (4/8)
Whole-brain connectivity graph based on representational similarity across individuals. Nodes represent cortical areas; edges reflect shared representational geometry. Two main subnetworks emerge along ventral and lateral visual pathways.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
We identified 3 cortical hubs with highly consistent representations across all individuals: 📍 Early visual cortex (V1–V4) 📍 Ventral hub (scene/object areas ~PPA) 📍 LOTC Hub (hMT+/TPOJ) These hubs form two pathways: - Classical ventral stream (EVC → Ventral) - Lateral stream (EVC → LOTC) ⬇️ (3/8)
Three brain regions show high inter-subject representational similarity: early visual cortex, ventral hub, and LOTC. A connectivity graph shows how these hubs are embedded in two distinct streams based on representational geometry.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
Using Representational Similarity Analysis (RSA) on fMRI data from people viewing diverse scenes, we measure: - Inter-subject RSA: Are visual representations shared across individuals? - Brain-Model RSA: Is this shared information low-level (visual) or high-level (semantic)? Methods ⬇️ (2/8)
Diagram showing how brain activity, vision models, and language models are compared using RSA to analyze representational alignment across stimuli, models, and brain regions.
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 25/07/2025
🧠🚨 How does the brain represent what we see? Is visual input transformed to form these representations in similar ways across people and even AI models like DNNs? We explore these questions using fMRI and large-scale representational alignment analyses. 🔗 arxiv.org/abs/2507.13941 Thread👇 (1/8)
arxiv.org
Convergent transformations of visual representation in brains and models
A fundamental question in cognitive neuroscience is what shapes visual perception: the external world's structure or the brain's internal architecture. Although some perceptual variability can be trac...
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Pablo Marcos-Manchón @jazzmaniatico.bsky.social · 23/04/2025
Deep learning models and brains share fascinating parallels in their ability to process and instantly integrate new knowledge. Join us this year at ICON 2025 to discuss how sudden learning emerges across artificial and biological systems! 🧠🤖
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Reposted by Pablo Marcos-Manchón
Marc Sabio Albert @msabio.bsky.social · 11/10/2024
Very happy to announce that the latest work "Anticipating multisensory environments: Evidence for a supra-modal predictive system" from @alepebel.bsky.social @fuentemilla.bsky.social and myself has been published in Cognition! www.sciencedirect.com/science/arti...
sciencedirect.com
Anticipating multisensory environments: Evidence for a supra-modal predictive system
Our perceptual experience is generally framed in multisensory environments abundant in predictive information. Previous research on statistical learni…
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