I have always believed that ideas from basic systems neuroscience could eventually help people. But for years that felt more like hope than reality. But look! This is one of the most personally satisfying papers my lab has produced because we’re getting there. www.biorxiv.org/content/10.6... 🧪🧵 1/

Now out in Nature Neuroscience: "Fixation duration on natural scenes is explained by memory encoding not processing demand". www.nature.com/articles/s41... Our eyes don't linger because recognition is hard; they linger to remember. Let me take you on a quick tour. 🧵

Fixation duration on natural scenes is explained by memory encoding not processing demand - Nature Neuroscience

By combining magnetoencephalography and eye tracking, this study sheds light on why people fixate on some parts of natural scenes longer than others. Rather than visual complexity, fixation durations ...

nature.com

Our NeuroAI study made it onto the cover of Nature Machine Intelligence ❤️. In it, we demonstrate that a developmentally-inspired visual diet can drastically improve the robustness of ANN-based vision systems. www.nature.com/articles/s42... open access, open code, open weights, open science.

Our May issue is live! With a study teaching AI human-like shape-based vision, a domain-adapted LLM to support clinical psychiatrists, an octopus-inspired robot arm for underwater tasks and more. Plus: Our editorial "Stop ‘tokenmaxxing’ and deploy AI sensibly instead"! www.nature.com/natmachintell/

I'm proud to say we are releasing LAION-fMRI, a densely sampled 7T fMRI dataset of natural images, with very broad stimulus sampling for testing countless hypotheses and for deeply exploring brain representations. The dataset is now available at laion-fmri.hebartlab.com What does LAION-fMRI offer? 🧵

LAION-fMRI - a 7T fMRI dataset of human vision

LAION-fMRI (LfMRI / LAION MRI dataset): 5 subjects, 25,052 launch-release natural images, 165 acquired 7T fMRI sessions with single-trial GLMsingle betas, retinotopy, localizers, and diffusion.

laion-fmri.hebartlab.com

Excited about our new preprint: “The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream” arxiv.org/abs/2604.12825 Work with Sushrut Thorat, Anna Mitola, Paolo Papale, Peter König & Tim Kietzmann 🧵 thread below

The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream

In studying primate vision, a large body of work focuses on the first feedforward sweep. During this initial time window, information is thought to pass through ventral stream regions in a stage-like ...

arxiv.org

Happy to announce the 3rd iteration of NEAT (Neuro-AI-Talks), which will take place in Osnabrück September 14th-15th 2026. NEAT is a (deliberately small scale) NeuroAI workshop that brings together researchers from neuroscience and AI. www.kietzmannlab.org/neat2026/ More information below 👇

NEAT 2026

kietzmannlab.org

What happens if you hook up an energy-efficiency optimising RNN on active vision input? It learns predictive remapping and path integration into allocentric scene coordinates. Now out in patterns: www.cell.com/patterns/ful...

Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision

This study gives an example of how complex computations in neural networks can emerge from simple physical principles. Training a model to optimize internal energy efficiency while performing eye move...

cell.com

Tim Kietzmann @timkietzmann.bsky.social · last yr.

Can seemingly complex multi-area computations in the brain emerge from the need for energy efficient computation? In our new preprint on predictive remapping in active vision, we report on such a case. Let us take you for a spin. 1/6 www.biorxiv.org/content/10.1...

🚨New Preprint! How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715 + @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social 🧵 1/14

Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex

Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...

arxiv.org

Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language. tl;dr: you can now chat with a brain scan 🧠💬 1/n

Want to make publication-ready figures come straight from Python without having to do any manual editing? Are you fed up with axes labels being unreadable during your presentations? Follow this short tutorial including code examples! 👇🧵

New results for a new year! “Linking neural population formatting to function” describes our modern take on an old question: how can we understand the contribution of a brain area to behavior? www.biorxiv.org/content/10.1... 🧠👩🏻‍🔬🧪🧵 #neuroskyence 1/

Linking neural population formatting to function

Animals capable of complex behaviors tend to have more distinct brain areas than simpler organisms, and artificial networks that perform many tasks tend to self-organize into modules (1-3). This sugge...

biorxiv.org