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Tim Kietzmann

@timkietzmann.bsky.social

ML meets Neuroscience #NeuroAI, Full Professor at the Institute of Cognitive Science (Uni Osnabrück), prev. @ Donders Inst., Cambridge University

107 Posts3386 Follower347 Folgt
Ryuto Yashiro@ryuto-yashiro.bsky.social · 2d ago

Excited to share our new paper: "Probing the content of semantic representations in body-selective regions" We propose a framework based on object co-occurrence to interpret semantic representations of natural scenes predicted by LLM embeddings. doi.org/10.1162/IMAG...

Probing the content of semantic representations in body-selective regions

Abstract. Recent advances in neural networks trained on natural language have revealed that category-selective regions encode complex semantics and contextual information of natural scenes in addition...

doi.org

Victoria Bosch@initself.bsky.social · 4w ago

Excited to share that our paper has been accepted for a talk at #CogSci2026: Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network Linda Ariel Ventura, Victoria Bosch, Tim C. Kietzmann, and Sushrut Thorat. Preprint: arxiv.org/abs/2602.03490. ⛓️

Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network

Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how ...

arxiv.org

Tim Kietzmann @timkietzmann.bsky.social · 2mo ago

NEAT (Neuro-AI-Talks) is fully booked! We are happy for the great interest and are looking forward to welcoming you all to Osnabrück in September!

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

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 👇

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

Ever wondered why we fixate some locations longer than others? We initially thought that it had to do with recognition complexity, and were quite wrong. In Nature Neuroscience we present an alternative take: the need to memorise governs fixation durations.

Philip Sulewski@psulewski.bsky.social · 3mo ago

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. 🧵

Philip Sulewski@psulewski.bsky.social · 3mo ago

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

Lucas C Parra@parralab.org · 3mo ago

This paper changed my mind on how visual processing works in the brain. We knew it was dynamic given all the recurrent connections. But I thought it would converge to a fixpoint during a fixation. Not so, it seems ... The dynamic captures different aspects of the stimulus at different times. Nice.

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

A huge effort and a new take on what constitutes the "feedforward pass" across the visual system. Make sure to check out our new preprint:

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

Looking for robust AI vision? Then this NeuroAI paper may be for you 👇 Open access, open code, open weights.

Zejin Lu@zejinlu.bsky.social · 3mo ago

Now out in Nature Machine Intelligence @NatMachIntell “Adopting a human developmental visual diet yields robust and shape-based AI vision”: doi.org/10.1038/s422.... A wonderful case where brain inspiration improved AI. With @martisamuser.bsky.social, Radek Cichy and @timkietzmann.bsky.social .

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

A huge effort and a new take on what constitutes the "feedforward pass" across the visual system. Make sure to check out our new preprint:

Daniel Anthes@anthesdaniel.bsky.social · 3mo ago

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

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

A huge effort and a new take on what constitutes the "feedforward pass" across the visual system. Make sure to check out our new preprint:

Daniel Anthes@anthesdaniel.bsky.social · 3mo ago

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

Katharina Dobs@kathadobs.bsky.social · 3mo ago

Highly recommended: I really enjoyed NEAT, every time I attended. Great opportunity to catch up on #NeuroAI, especially for those who can't make it to CCN!

Tim Kietzmann @timkietzmann.bsky.social · 3mo ago

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 👇

Philip Sulewski@psulewski.bsky.social · 9mo ago

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...

Itamar Avitan@avitanit.bsky.social · 9mo ago

Our work reveals a sharp trade-off between predictive accuracy and model identifiability. Flexible mappings maximize predictivity, but blur the distinction between competing computational hypotheses.

Thomas Nortmann@thonor.bsky.social · 9mo ago

🚨 Out in Patterns! We asked ourselves, if complex neural dynamics like predictive remapping and allocentric coding can emerge from simple physical principles, in this case Energy Efficiency. Turns out they can! More information in the 🧵 below. I am super excited to see this one out in the wild.

A screenshot of the article "Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision". The authors are Thomas Nortmann, Philip Sulewski and Tim C. Kietzmann. Under the article heading is a section titled "The bigger picture". The section reads as follows: "We show how some of the brain’s amazing abilities, such as keeping our view of the world stable even when our eyes move, might come from simple ideas such as saving energy. Instead of assuming that the brain is wired with complex instructions for predicting what we will see next, we explored whether these skills could develop naturally from basic physical principles. We apply a computer model that mimics how our eyes move and how the brain processes visual information. This model was trained to perform eye movements while trying to use as little energy as possible by reducing unnecessary neural activity. Surprisingly, as the model learned to be more energy efficient, it started to develop a process called predictive remapping. This process is how the brain predicts what will appear in our vision after an eye movement, so our perception stays smooth and stable. Moreover, the model learned to create an internal representation that translates the position of the eyes into a more stable, environment-centered frame of reference. This internal map helps the system predict future visual input and decide when to inhibit or reduce certain signals, making the whole process more efficient. Altogether, we show that complex visual functions such as predictive remapping and creating an environment-centered reference frame can emerge naturally when a system is optimized for energy efficiency."
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...

Tim Kietzmann @timkietzmann.bsky.social · 9mo ago

We went back to the drawing board to think about what information is available to the visual system upon which it could build scene representations. The outcome: a self-supervised training objective based on active vision that beats the SOTA on NSD representational alignment. 👇

Sushrut Thorat@martisamuser.bsky.social · 9mo ago

🚨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

Université de Montréal - News@umontreal-en.bsky.social · last yr.

#AI "Ultimately, this is a step forward in understanding how the human brain understands meaning from the visual world." #LLMs @mila-quebec.bsky.social @adriendoerig.bsky.social @timkietzmann.bsky.social @natmachintell.nature.com nouvelles.umontreal.ca/en/article/2...

Using AI to 'see' what we see

Fed the right information, large language models can match what the brain sees when it takes in an everyday scene such as children playing or a big city skyline, a new study led by Ian Charest finds.

nouvelles.umontreal.ca