Floris de Lange

We have three lectureships available at York Psychology (@yorkpsychology.bsky.social) with a broad remit for research/teaching areas. Home and overseas applications are welcome. Deadline for applications is early April - enquiries welcome. Come and join us! jobs.york.ac.uk/vacancy/lect...

Very happy to share our review on Reinforcement Learning vs Statistical Learning, with @ambrafer.bsky.social and @predictivebrain.bsky.social:

www.sciencedirect.com/science/arti...

A nice summary:
www.sainsburywellcome.org/blog/two-eng...

Aphantasia and visual working memory: No direct evidence of impaired visual working memory in aphantasics, either in behavioral performance or the accuracy of a multivoxel pattern classifier Visual mental imagery and visual working memory are often thought to be closely related. After all, both have been argued to involve the temporary mai…

#Aphantasia and visual working memory: No direct evidence of impaired visual working memory in aphantasics, either in behavioral performance or the accuracy of a multivoxel pattern classifier www.sciencedirect.com/science/arti...

Registration and abstract submission for our symposium Naturalistic Neuroscience – from perception to action and back (University of Bonn 28-29 May 2026) is no open!
www.nn2026.uni-bonn.de/en/registrat...

#NN2026 @unibonn.bsky.social

#naturalisticneuroscience #neuroscience #symposium #cfa

Compact deep neural network models of the visual cortex Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

DNN models of the brain are getting bigger. Are we replacing one complicated system in vivo with another in silico?

In new work, we seek the *smallest* DNN models of visual cortex, balancing prediction with parsimony.

It turns out these compact models are surprisingly small!

rdcu.be/e5H8G

Making the Brain Language Ready: A Journey of Discovery For more than four decades, CNS 2026 keynote speaker Peter Hagoort has focused on one key question: What makes the human brain language ready?

What a wonderful keynote by Peter Hagoort! For more information on Hagoort's work, read this exclusive Q&A on the CNS blog:
www.cogneurosociety.org/making-the-b...
#CNS2026 10/ end 🧵

Task learning increases information redundancy of neural responses in macaque visual cortex How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests that learning reduces redundancy in neural representations to improve efficiency, whereas anot...

RIP redundancy reduction?

Beautiful work by Liu & colleagues showing that neural redundancy increases with learning, as predicted by a Bayesian model:
www.science.org/doi/10.1126/...

Recurrent cortical networks encode natural sensory statistics via sequence filtering The visual cortex receives a stream of high-dimensional sensory input. The role of dense local, recurrent cortical connections in shaping responses to these inputs has been unclear. Here, we show that...

Recurrent cortical networks encode natural sensory statistics via sequence filtering
www.cell.com/neuron/abstr...
#neuroscience

Great opportunity to work in a terrific lab:

Orbitofrontal cortex drives predictive filtering of sensory responses - Nature Neuroscience Top-down projections from the orbitofrontal cortex carry predictive signals that grow with sound experience and suppress the auditory cortex via inhibitory circuits, revealing a predictive mechanism f...

Orbitofrontal cortex drives predictive filtering of sensory responses

www.nature.com/articles/s41...

#neuroskyence

Why are tactile sensations suppressed during movements? In our new preprint, we explain this as optimal integration of sensory signals with an internal model.

Work led by @fatatai.bsky.social with Dimitris Voudouris, Katja Fiehler and @c-rothkopf.bsky.social

www.biorxiv.org/content/10.6...

New Preprint alert ❗

Holistic and kinematics priors enable predictive neural representations during biological motion perception

With: @predictivebrain.bsky.social and
@moritzwurm.bsky.social

At: doi.org/10.1101/2025...

Forward and backward prediction in learning and perception Predictive processing frameworks have emphasized the role of forward prediction as a critical ingredient for learning and perceptual inference. We ant…

These findings align well with what @clarepress.bsky.social and I discuss in our "Forward and backward prediction" paper, see www.sciencedirect.com/science/arti...

What type of relationship is learned during visual statistical learning? Statistical learning enables observers to extract regularities from their environment, but what statistical regularity is extracted remains debated. While previous research has mainly focused on condi...

Which "statistic" governs statistical learning? Find out in this new article of our lab: journals.plos.org/plosone/arti...

Thrilled to finally share this work! 🧠🔊

Using a new reinforcement-free task we show mice (like humans) extract abstract structure from sound (unsupervised) & dCA1 is causally required by building factorised, orthogonal subspaces of abstract rules.

Led by Dammy Onih!
www.biorxiv.org/content/10.6...

Our paper is out in @natneuro.nature.com!

www.nature.com/articles/s41...

We develop a geometric theory of how neural populations support generalization across many tasks.

@zuckermanbrain.bsky.social
@flatironinstitute.org
@kempnerinstitute.bsky.social

1/14

📢 Workshop announcement.

We are super excited to announce the workshop Perceptual Inferences, from philosophy to neuroscience, organized by Alexander Schütz and Daniel Kaiser.

📍 Rauischholzhausen Castle, near Marburg, Germany
🗓️ June 8 to 10, 2026.
1/4

Interesting convergence:

The trick that made predictive self-supervised vision models work seems to be what the brain was doing all along

w/ @predictivebrain.bsky.social: visual cortex is most sensitive to high-level prediction errors -- even in V1

Now published:
journals.plos.org/ploscompbiol...

Forward and backward prediction in learning and perception Predictive processing frameworks have emphasized the role of forward prediction as a critical ingredient for learning and perceptual inference. We ant…

"Forward and backward prediction in learning and perception"

@predictivebrain.bsky.social @clarepress.bsky.social

nice to see our work situated within a broader research program on differences between learning & perceptual predictions

www.sciencedirect.com/science/arti...

A unifying account of replay as context-driven memory reactivation A context-driven memory model simulates a wide range of characteristics of waking and sleeping hippocampal replay, providing a new account of how and why replay occurs.

Really thrilled that this paper led by @neurozz.bsky.social is now published in its final version in @elife.bsky.social!!

This is a memory-focused (as opposed to RL-focused) account of the detailed characteristics of forward and backward awake and sleep replay!

elifesciences.org/articles/99931

Uruguay’s Renewable Charge: A Small Nation, A Big Lesson For The World Uruguay built a power grid that runs 99% on renewables—at half the cost of fossil fuels. Here’s how its bold energy overhaul became a global model.

“Uruguay did what most nations still call impossible: it built a power grid that runs almost entirely on renewables—at half the cost of fossil fuels. The physicist who led that transformation says the same playbook could work anywhere—if governments have the courage to change the rules.”

With some trepidation, I'm putting this out into the world:
gershmanlab.com/textbook.html
It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class.

My hope is that this will be a living document, continuously improved as I get feedback.

🧠 Feature-specific predictive processing: What’s in a prediction error? 🧠

Perspective article w/ Cem Uran, @martinavinck.bsky.social & @predictivebrain.bsky.social now in @imagingneurosci.bsky.social, highlighting recent work on the nature of surprise reflected in visual prediction errors.

🧵👇

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

Rep. Seth Moulton comes on CNN and starts by saying: "Is anyone going to just stop for a second and be honest? This is insane. What the hell are we doing? We've got a lot of problems in America today, and invading / occupying / running Venezuela does not solve any of them."

Me getting back to sending nagging emails on January 6th when people return to work from the Christmas break

Leveraging insights from neuroscience to build adaptive artificial intelligence Nature Neuroscience - Adaptive intelligence envisions AI that, like animals, learns online, generalizes and adapts quickly. This Perspective reviews biological foundations, progress in AI and...

Interested in the latest advances in neuroscience (neural dynamics and internal models) and how they can be leveraged to build smarter, adaptive AI?

➡️ My first real solo piece 🖤🫶 @natneuro.nature.com

rdcu.be/eWVmA

There's been a cluster of recent work on these sort of flow based or trajectory based world models, including nova flow and amplify (amplify a past episode of ours). These are super exciting because they reduce requirements for really accurate action labels imo

Better artificial intelligence does not mean better models of biology Deep neural networks (DNNs) once showed increasing alignment with primate perception as they improved on vision benchmarks, raising hopes that advances in artificial intelligence (AI) would naturally yield better models of biological vision. However, we present accumulating evidence that this alignment is now plateauing – and in some cases worsening – as DNNs scale to human or even superhuman accuracy. This divergence between artificial and biological perception may reflect the acquisition of visual strategies distinct from those of primates, and these findings challenge the view that advances in AI will naturally translate to progress in neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for internet data.

Online Now: Better artificial intelligence does not mean better models of biology

Ok, this is nuts. Once you see it you cannot unsee it. Do you see it?
(OP @drgbuckingham.bsky.social )