Our work on curriculum learning in humans and ANNs is now out in PLOS Computational Biology. Thanks to excellent feedback from the reviewers and editor, the final paper now contains some cool new findings.Take a look! 🧠🤖

Charlotte Volk@charlottevolk.bsky.social · yesterday

Excited to share that our paper has now been published in PLOS Computational Biology! journals.plos.org/ploscompbiol...

Excited to share that our paper has now been published in PLOS Computational Biology! journals.plos.org/ploscompbiol...

The curriculum effect in visual learning: The role of readout dimensionality

Author summary Learning new skills is fundamental to humans and animals. However, a key challenge in learning is generalization: applying learned skills to new situations not encountered during traini...

journals.plos.org

Charlotte Volk@charlottevolk.bsky.social · 10mo ago

🚨 New preprint alert! 🧠🤖 We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up. 🧠📈 A 🧵: tinyurl.com/yr8tawj3

Our work on curriculum learning in humans and ANNs is now out in PLOS Computational Biology. Thanks to excellent feedback from the reviewers and editor, the final paper now contains some cool new findings.Take a look! 🧠🤖

Charlotte Volk@charlottevolk.bsky.social · yesterday

Excited to share that our paper has now been published in PLOS Computational Biology! journals.plos.org/ploscompbiol...

The apical dendrite looks great, and does wonders in a slice. What does it do in the living cortex? It depends on the cell. A causal spatial role for apical dendrites in the cortex www.biorxiv.org/content/10.6... by @anyiliu.bsky.social with @kenneth-harris.bsky.social and @fedrossi.bsky.social

🎉 *extremely* excited to share our human single neuron + movie dataset is out in #ScientificData! 📄 Human neuron activity during an 83-minute movie from 2,286 neurons and 29 patients 🔗https://www.nature.com/articles/s41597-026-07955-0 1st public dataset from @humansingleneuron.bsky.social!🧵

🔮At #ICML2026 we presented Attentive Multi-Layer Fusion for Vision Transformers, an efficient method for decoding information from multiple layers of ViTs for downstream task predictions. It’s almost as performant as full fine-tuning but only an inch more expensive than linear probing!

ExplainableML@eml-munich.bsky.social · last mo.

8/ ALF: Attentive Multi-Layer Fusion for ViTs @lciernik.bsky.social , Marco Morik, @zeynepakata.bsky.social , @lukasmut.bsky.social [Paper] openreview.net/forum?id=wSXZtvqwoa [Code] github.com/lciernik/attentive-layer-fusion

Exactly @gershbrain.bsky.social! But more importantly: these mice are overtrained on this one task. Maybe stuff gets more broadly distributed across the brain if it is the one thing you do all day every day. And, they are MICE with tiny brains! So, important study, but let's not over-generalize.

Sam Gershman@gershbrain.bsky.social · 2w ago

This is a really interesting and important study: www.nature.com/articles/s41... However, isn't it concerning that sweeping conclusions about functional organization of cortex are being reached on the basis of a single task?

Looking to the brain to improve energy efficiency of AI when we started 3 years ago, i thought if we didn't pull it off in a couple of months, the topic would be soon out of fashion of coz, long distance collab -> we stalled ... but strangely, it isn't totally irrelevant yet. what does this mean?

megan peters 🧠@meganakpeters.bsky.social · 2w ago

I am *thrilled* to share this review in @currentbiology.bsky.social out today, by yours truly, @myoo.bsky.social, @michalk.bsky.social, Taro Toyoizumi, @tyrellturing.bsky.social, @taylorwwebb.bsky.social, & @hakwan.bsky.social www.sciencedirect.com/science/arti... 🧵👇

I am *thrilled* to share this review in @currentbiology.bsky.social out today, by yours truly, @myoo.bsky.social, @michalk.bsky.social, Taro Toyoizumi, @tyrellturing.bsky.social, @taylorwwebb.bsky.social, & @hakwan.bsky.social www.sciencedirect.com/science/arti... 🧵👇

Looking to the brain to improve energy efficiency of AI

Modern artificial intelligence (AI) systems have achieved remarkable capabilities, but at an extraordinary energy cost. Training and running large-sca…

sciencedirect.com

Mixed feelings from #NeurIPS ACs on the new task of writing initial meta-reviews. Yeah... "it's yet another task for ACs", "why do what authors are supposed to do"... I'm there with you. But on the plus side, it forces ACs to engage earlier and better with discussions. Right?

New Preprint! We gave neural networks infant-like curiosity and let them free to explore. They grew brain-like architecture, followed human synaptic development, and even started to display compositional thought. So we think curiosity doesn't just come from a complex mind... it helps us build one 👇

Curiosity shapes brain-like architectures and functions

How does complex cognition emerge from simpler underlying processes? We show that two components are sufficient: infant-like curiosity and brain-like biophysical constraints jointly drive the emergenc...

biorxiv.org

New preprint (well, very updated). 🤖🧠🧪 We find that an abstract model of neuromodulation lets spiking neural networks perform much better, particularly in challenging noisy environments, using less energy. Relevant to #neuroscience and #neuromorphic computing. 🧵👇 www.biorxiv.org/content/10.1...

Neuromodulation enhances the capability and efficiency of spiking neural networks

Spiking neurons underlie the brain’s extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency in practice has proven challenging. We ...

biorxiv.org

Great paper! could someone please point me to a paper or two that has seriously pushed for ideas that would be in contradiction with this decoder? I know @benhayden.bsky.social, @pessoabrain.bsky.social et al have done that on bluesky but an actual research paper? Feels like a total straw man

Shahab Bakhtiari@shahabbakht.bsky.social · 3w ago

Important paper: "The response profiles of neurons in different brain regions differ enough that a decoder can determine the region to which a particular neuron belongs." Sounds like the perfect antidote to the "everything is everywhere" view.

Important paper: "The response profiles of neurons in different brain regions differ enough that a decoder can determine the region to which a particular neuron belongs." Sounds like the perfect antidote to the "everything is everywhere" view.

@stefanofusi.bsky.social · 3w ago

From a great collaboration with @lorenzoposani.com, @shuqiw.bsky.social, Samuel Muscinelli, Liam Paninski, now in Nature: www.nature.com/articles/s41...

From a great collaboration with @lorenzoposani.com, @shuqiw.bsky.social, Samuel Muscinelli, Liam Paninski, now in Nature: www.nature.com/articles/s41...

Rarely categorical, highly separable representations along the cortical hierarchy - Nature

Cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared towards high-dimensional, highly separable neural representations.

nature.com

Do people solve hard problems by first building a mental model, then searching for the best solution? With @gershbrain.bsky.social, our new manuscript suggests a different route: people often start by retrieving cached solutions, and scaffold representations around them. osf.io/preprints/ps...

Many people are complaining about Anthropic’s J-space work, saying it's anthropomorphizing linear algebra, why even bring up consciousness, etc. But honestly, I’m so happy to see a framework that actually puts abstract theories like GWT to the test in-silico and exposes their conceptual limits.

Anthropic {bot}@anthropicbot.bsky.social · last mo.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. (1/2)

Pretraining + fine-tuning powers modern ML, but we lack a theoretical understanding of how pretraining actually shapes downstream learning. In our new @icmlconf.bsky.social paper, we address this gap! 📅 July 9th, Poster #4502 Session 8! 🧵 arxiv.org/pdf/2602.20062

(1/6) My first first-author paper is now out in @nconsc.bsky.social! Part of my PhD work with @mamassian.bsky.social at @lsp-ens.bsky.social. We looked at how confidence behaves during a perceptual learning task with no feedback. academic.oup.com/nc/article/2...

Perceptual learning without feedback is accompanied with systematic changes in confidence processing

Abstract. Perceptual learning can occur without external feedback, raising questions about what internal signals might guide this learning. Here, we test t

academic.oup.com

Now out at PNAS - congratulations to Ramanujan Srinath and the team! Ram's ability and willingness to connect all these data, models, and ideas has changed my thinking/interpretation of many findings. I suspect I will keep learning from this study for a long time. www.pnas.org/doi/10.1073/...

The structure of correlated variability reflects task-relevant information in sensory neurons | PNAS

Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low dime...

pnas.org

Marlene Cohen@marlenecohen.bsky.social · 12mo ago

One of the joys of being a scientist is the ability to think about a problem for a long time. Our new preprint solves a mystery that has been bugging me since I was a graduate student (which was, ahem, a while ago). 🧪🧠🧵1/ www.biorxiv.org/content/10.1...

Super excited to share a new preprint! We asked a simple-but-big question: What changes in the brain when someone becomes an expert? Using chess ♟️ + fMRI 🧠 + representational geometry & dimensionality 📈, we ask: 1️⃣ WHAT information is encoded? 2️⃣ HOW is it structured? 3️⃣ WHERE is it expressed? 1/n

Today at ICML2026, Jack King presents our work with @evfedorenko.bsky.social, linking internal representation trajectories to LLM behavior. 📄 Representational Curvature Modulates Behavioral Uncertainty in LLMs 🔗https://arxiv.org/abs/2604.23985 📍Hall A #1903 | ⏰ 10:30a-12:15p KST Come say hi!

Our paper "Neural timescales from a computational perspective" is finally out in @natneuro.nature.com: www.nature.com/articles/s41... We discuss how computational models and methods can distill empirical observations on neural timescales into quantitative, testable theories of brain computation.

Neural timescales from a computational perspective - Nature Neuroscience

This review integrates computational approaches to provide a unified view on how data analysis methods, biophysical mechanistic models and machine learning approaches can help to uncover the origins a...

nature.com

For a long time I felt like there was a hesitation in neuroscience to ascribe anything like a planning function to the various "replay" events we see in the hippocampus, but I think this is rapidly changing. Here's another new entry in human epilepsy patients: www.nature.com/articles/s41... 🧠📈 🧪

Human hippocampal ripples coordinate planning sequences and compositional representations in neocortex - Nature Neuroscience

Human hippocampal ripples and replay interact with the prefrontal cortex to update mental representations online, letting the brain compositionally combine familiar elements in new ways for flexible p...

nature.com

Very excited to share this paper, led by @ajhaskins.bsky.social ! We find that how people look around the world is stable and idiosyncratic — and that these gaze patterns are shaped in part by the conceptual priorities each person brings to a scene. 1/3 www.pnas.org/doi/10.1073/...

(1/n) Thrilled to share my first paper at Meta FAIR! "EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data" 👶 Human infants learn language from sparse, noisy multimodal input. Today's VLMs can't. We built a benchmark + challenge to close that gap. 🧵

Updated preprint and code from my lab. If you want to explore more realistic neural dynamics in models that can still perform visual tasks, check it out! www.biorxiv.org/content/10.1...

Modeling Dynamical Vision with Biologically Plausible Recurrent Convolutional Networks

Convolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectu...

biorxiv.org

This work was inspired by @tomerullman.bsky.social's “illusion illusion” paradigm (arxiv.org/abs/2412.18613) where VLMs mistake illusion-like images for genuine optical illusions. In both cases, models respond to what a problem looks like rather than what it actually requires.🔮

The Illusion-Illusion: Vision Language Models See Illusions Where There are None

Illusions are entertaining, but they are also a useful diagnostic tool in cognitive science, philosophy, and neuroscience. A typical illusion shows a gap between how something "really is" and how some...

arxiv.org

New paper! w/ @cocoscilab.bsky.social🧵Can large language models reason flexibly, or have they learned what reasoning looks like? We introduce a new paradigm to test this question—the riddle riddle—and find that humans and LLMs show opposite patterns of performance. 📜

Lovely to see the full range of excellent commentaries on our BBS article with @matthiasmichel.bsky.social, together with our response, now out: Target article: www.cambridge.org/core/journal... Commentaries: www.cambridge.org/core/journal... Our response: www.cambridge.org/core/journal...

Matthias Michel@matthiasmichel.bsky.social · last yr.

Very happy to announce that our paper “Sensory Horizons and the Functions of Conscious Vision” is now out as a target article in BBS!! @smfleming.bsky.social and I present a new theory of the evolution and functions of visual consciousness. Article here: doi.org/10.1017/S014.... A (long) thread 🧵