The New Year is starting with good news: Check out our second article dealing with interpersonal predicitve coding thanks to great team work @PSauseng Anna Lena Biel Leonhard Schilbach Johannes Singer, Dariusz O'Leary, Eva Victoria Seegenschmiedt and Imme Zillekens! @munich_biopsych

Happy to share our new preprint biorxiv.org/content/10.110…. Together with Radek Cichy and Daniel Kaiser, we found feedback in the alpha frequency across the visual hierarchy as a key mechanism for integrating spatiotemporally consistent information in natural vision.

I'm thrilled that THINGS-data is now online! We provide 3 massive datasets of fMRI, MEG, & behavior in response to up to 1854 objects and >22k images. We hope this will allow studying objects in vision, memory & language with unique semantic breadth! Link: doi.org/10.25452/figsh…

I hope you are enjoying #VSS2023! In case you are interested in what our lab has been up to, check out these presentations! 👇 Work with Marie St-Laurent courtois-neuromod @kateiyas Johannes Singer Oliver Contier Judy Fan Wilma Bainbridge Kushin Mukherjee Johannes Roth Laura Stoinski

Come by my poster tomorrow 8:30-12:30AM Banyan Breezeway if you're interested in the link between scene representations in the brain and behavior! #VSS

Our paper on neuroconnectionism is finally out in Nature Rev Neurosci! After years of work with an amazing team of co-authors and reviewers we are proud to present our thoughts on the philosophical and scientific underpinnings of using ANNs in neuroscience. rdcu.be/ddkIe 🧵

How does our brain enable us to make sense of our visual world? 🗺👀🧠 Excited to share our latest work with Chris Baker and Martin Hebart : “Distributed representations of behaviorally relevant object dimensions in the human visual system” biorxiv.org/content/10.110… 🧵 1/n

And it's out in Journal of Cognitive Neuroscience! We added a feedforward CNN control and showed that for certain stimuli, recurrence can help with better predicting scene categorization in humans in terms of neural reps, behaviour and the brain-behaviour link: doi.org/10.1162/jocn_a…

🚨 CCN paper w/ Adrien Doerig Tim Kietzmann - arxiv.org/abs/2308.12435 The brain is a recurrent processor and RNNs mirror this, performing well at object recognition. In such RNNs, what representations and dynamics underlie the time it takes to classify different images? 🧵1/10

Now it's out in Science Advances science.org/doi/10.1126/sc…. Using EEG and fMRI data, we found that top-down feedback mediated by cortical alpha dynamics plays a key role in the construction of coherent visual experiences. With Daniel Kaiser and Radek Cichy. Center for Cognitive Neuroscience Berlin

Glad to share that our paper is out in JN SfN Journals. Here, we explored the common and unique associations between neural representational similarity and different types of visual search using fMRI, MEG, and a DNN. With Marius Peelen Sushrut Thorat 👇jneurosci.org/content/44/12/…

Excited to announce the second iteration of NEAT (Neuro-AI-Talks), which will take place September 2nd-3rd 2024 in Osnabrück. kietzmannlab.org/neat2024 Never heard of it? Let me tell you what this is about 🧵

1/13 Heavily updated preprint! arxiv.org/abs/2209.11737 We show that the contextual information encoded in Large Language Models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. 🧵

New preprint on the representational nature of spatio-temporal recurrent processing in visual object recognition led by Siying Xie!

So happy to have arrived at CogCompNeuro - Find me today at Poster A153 presenting my work using frozen LLMs for neural decoding of vision and analysing transformer attention on the neural data!

Come by our poster at #CCN2024 if you're interested in how visual representations might be read out from the ventral visual stream in a task-dependent fashion! Poster C7, Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink