Lenny van Dyck

New Correspondence with @davidpoeppel.bsky.social in Nat Rev Neurosci. www.nature.com/articles/s41...

Here, we critique a recent paper by Rosas et al. We argue that "Bottom-up" and "Top-down" neuroscience have various meanings in the literature.

PDF: rdcu.be/eSKYI

Investigating individual-specific topographic organization has traditionally been a resource-intensive and time-consuming process. But what if we could map visual cortex organization in thousands of brains? Here we offer the community with a toolbox that can do just that! tinyurl.com/deepretinotopy

Y’all are reading this paper in the wrong way.

We love to trash dominant hypothesis, but we need to look for evidence against the manifold hypothesis elsewhere:

This elegant work doesn't show neural dynamics are high D, nor that we should stop using PCA

It’s quite the opposite!

(thread)

Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social

Really looking forward to #CCN2025!

On Tuesday, I'm presenting new work with @kathadobs.bsky.social on segregated vs. integrated face & body processing in visual cortex 😊🧍🧠

Using DNNs & fMRI, we test competing hypotheses, finding both distinct & shared selectivity.

Come by Poster A64 for more.

Very much looking forward to #CCN2025! Would love to see you at our lab's talks and posters, and meet me at the panel discussion in the Algonauts session on Wednesday!

Investigating action topography in visual cortex and deep artificial neural networks High-level visual cortex contains category-selective areas embedded within larger-scale topographic maps like animacy and real-world size. Here, we propose action as a key organizing factor shaping vi...

New preprint out! We propose that action is a key dimension shaping the topographic organization of object categories in lateral occipitotemporal cortex (LOTC)—and test whether standard and topographic neural networks capture this pattern. A thread:

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

🧵 1/n

YouTube video by McGovern Institute Things and Stuff: How the brain distinguishes oozing fluids from solid objects

Super excited to share our new article: “Dissociable cortical regions represent things and stuff in the human brain” with @nancykanwisher.bsky.social, @rtpramod.bsky.social and @joshtenenbaum.bsky.social

Video abstract: www.youtube.com/watch?v=B0XR...

Paper: authors.elsevier.com/a/1lWxv3QW8S...

Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective How the human brain supports diverse behaviours has been debated for decades. The canonical view divides visual processing into distinct "what" and "where/how" streams – however, their origin and inde...

🧠 NEW PREPRINT
Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective
www.biorxiv.org/content/10.1...

Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results.

www.nature.com/articles/s42...

Representation of locomotive action affordances in human behavior, brains, and deep neural networks | PNAS To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immed...

In these tumultuous times, still happy to report a scientific achievement: our preprint on affordance perception was just published in PNAS!

www.pnas.org/doi/10.1073/...

Using behavior, fMRI and deep network analyses, we report two key findings. To recapitulate (preprint 🧵lost on other place):

Thanks so much, Nick! I just read your latest preprint the other day and already noted it for the next version. Super relevant work :)

This work resonates with recent proposals by @meenakshikhosla.bsky.social, @taliakonkle.bsky.social, @jacob-prince.bsky.social, @cibaker.bsky.social, @jbrendanritchie.bsky.social, @olivercontier.bsky.social and many others.

Check out the preprint for details. We’d love to hear your thoughts!

11/11

This multidimensional framework supports both discrete category selectivity and continuous feature integration, offering a unified account of high-level visual cortex organization.

10/n

So here's our takeaway.

When analyzed in a data-driven manner, the two views aren't mutually exclusive but rather complementary. Individual dimensions form sparse feature-selective clusters but also contribute to distributed maps across cortex.

9/n

Functional tuning maps of individual dimensions from category-selective areas across cortex.

Individually, they followed a striking topography.

📍 Distinct subclusters within category-selective areas
🌐 But sparsely distributed maps across cortex

Local specialization meets global distribution.

8/n

Prediction performance of dimensions from category-selective areas across cortex.

Collectively, the dimensions from each area explained activity both within their area but also across broader regions of visual cortex.

7/n

Interpretability of dimensions in category-selective areas.

These dimensions captured diverse information.

🎯 Many aligned with each area’s preferred category (e.g., bodies in EBA)
🧩 Others encoded finer subcategory features (e.g., body parts)
🔄 Some even reflected cross-category distinctions (e.g., food vs. text)

6/n

Consistency of dimensions in category-selective areas.

We found that each area encoded multiple interpretable dimensions, consistent across individuals and primarily tuned to high-level semantic content.

Strikingly, even the most category-selective voxels showed this multidimensional tuning.

5/n

Data-driven fMRI voxel decomposition of category-selective areas.

To test this, we analyzed fMRI responses to thousands of natural images within classical category-selective areas using a data-driven decomposition approach.

Would the resulting organization look modular, continuous, or like something in between?

4/n

On the other side, a dimensional view argues that it is organized by a continuous feature space with distributed maps spanning across cortex.

Can these seemingly opposing views be reconciled?

3/n

During my studies, I got interested in a long-standing debate in visual neuroscience.

On one side, a categorical view holds that high-level visual cortex is composed of discrete modules specialized for domains like faces, bodies, and scenes.

2/n

How is high-level visual cortex organized?

In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈

www.biorxiv.org/content/10.1...
#neuroskyence

🧵 1/n

What shapes the topography of high-level visual cortex?

Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut.

🧵 ↓ 1/n

2 PHD-STUDENTS IN NEUROAI OF DEVELOPMENTAL VISION (M/F/X) - Academic Positions Join an interdisciplinary team to research developmental vision in NeuroAI. Requires a strong computational background, deep learning skills, and a Master's ...

There are 2 PhD positions in my lab in Amsterdam (collaboration with Sander Bohte, @tessamdekker.bsky.social and Ingmar Visser) on NeuroAI of developmental vision.

academicpositions.nl/ad/centrum-w...

Announcement: Workshop at #CCN2025
🧠 Modeling the Physical Brain: Spatial Organization & Biophysical Constraints
🗓️ Monday, Aug 11 | 🕦 11:30–18:00 CET | 📍 Room A2.07
🔗 Register: tinyurl.com/CCN-physical...
#NeuroAI @cogcompneuro.bsky.social

What are the organizing dimensions of language processing?

We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals

Beyond happy for you, Katha! You absolutely crushed it! 🥳🤗💪

Excited to share on my first post on Bluesky our new paper in NHB examine the innate and developing aspects of the wiring of the visual system. Congratulations to @emilykubota.bsky.social and the baby MRI team on this important work