@levandyck

PhD candidate in CogCompNeuro at @jlugiessen Exploring brains, minds, and worlds 🧠💭🗺️ https://xitter.cf/t.co/RM7RXTYQVd

 Munich/Gießen, Germany
levandyck.github.io/
Joined July 2022
  • Tweets57
  • Following282
  • Followers174
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Our review on the theoretical status of oscillations and field potentials is out! What are their causal effects, and what can electrophysiology signals reveal about how the brain works? w/ @dlevenstein @prokraustinator Bradley Voytek @_rdgao cell.com/trends/cognitive-sc…

Processes and measurements: a framework for understanding neural oscillations in field potentials

Various neuroscientific theories maintain that brain oscillations are important for neuronal computation, but opposing views claim that these macroscale dynamics are ‘exhaust fumes’ of more relevant...

cell.com
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"Rethinking category-selectivity in human visual cortex" with @susanwardle @mvazirip Dwight Kravitz @Chris_I_Baker 1/3
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Tim Kietzmann@TimKietzmann1 Nov 2024
🧵Join us for a short tour of how a large-scale MEG dataset collected during active vision made us question it all. New work with @carmen_amme @PhilipSulewski @EelkeSpaak @martin_hebart @konigpeter biorxiv.org/content/10.1101/…

Saccade onset, not fixation onset, best explains early responses across the human visual cortex...

Visual processing has traditionally been investigated using static viewing paradigms, where participants are presented with streams of randomized stimuli. Observations from such experiments have been...

biorxiv.org
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Martin Hebart@martin_hebart9 Sep 2024
How does our brain allow us to make sense of our visual world? Work led by @OliverContier w/ @Chris_I_Baker now out in @NatureHumBehav provides support for a theoretical framework of behaviorally-relevant selectivity beyond high-level categorization. 🧵 xitter.cf/OliverContier/status/1…
Oliver Contier@OliverContier9 Sep 2024
Hugely excited that this work with @martin_hebart and @Chris_I_Baker is now out in @NatureHumBehav !!! By moving from a category-focused to a behaviour-focused model, we identified behaviourally relevant object information throughout visual cortex. nature.com/articles/s41562-0…

Distributed representations of behaviour-derived object dimensions in the human visual system

Nature Human Behaviour - Contier et al. show that dimensions are superior to categories at predicting brain responses to visual objects.

nature.com
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Hugely excited that this work with @martin_hebart and @Chris_I_Baker is now out in @NatureHumBehav !!! By moving from a category-focused to a behaviour-focused model, we identified behaviourally relevant object information throughout visual cortex. nature.com/articles/s41562-0…

Distributed representations of behaviour-derived object dimensions in the human visual system

Nature Human Behaviour - Contier et al. show that dimensions are superior to categories at predicting brain responses to visual objects.

nature.com
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Kaiser Lab@DKaiserlab5 Sep 2024
Apply now for a 3-year postdoc position in the lab! Please retweet and share with potential candidates. If you're interested in #vision, #EEG, #fMRI, and working with a fun team at the @jlugiessen, feel free to get in touch before applying. Link: uni-giessen.de/de/ueber-uns/…
Kaiser Lab@DKaiserlab20 Jun 2024
We will soon advertise a 3-year postdoc position in the lab. We're looking for a person that's interested in understanding natural vision, has experience with EEG/fMRI and experience with state-of-the-art analysis methods. Please share and get in touch if you're interested! 🧠💜
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Zirui Chen@ziruichen4426 Aug 2024
Why do varied DNN designs yield equally good models of human vision? Our preprint with @michaelfbonner shows that diverse DNNs represent images with a shared set of latent dimensions, and these shared dimensions turn out to also be the most brain-aligned. arxiv.org/abs/2408.12804

Universal dimensions of visual representation

Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they learn universal...

arxiv.org
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Tuesday 27th (11:30 Aussie time) I'll share some initial results on my project on neural representations in the macaque visual system. Work with @martin_hebart & others. Check out my talk if you're at @biomag2024. And I'll be around in Sydney until 10th Sept if you want to chat!
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Katharina Dobs@KathaDobs8 Jul 2024
Really enjoyed writing a research highlight @sigtrans_sttt nature.com/articles/s41392-0… with the great @levandyck and Frank Bremmer about a recent @CellCellPress study by @a_marinvargas, @axlbsi and the @TrackingPlumes lab. Such inspiring work combining #NeuroAI and #proprioception!

Artificial intelligence meets body sense: task-driven neural networks reveal computational princi...

Signal Transduction and Targeted Therapy - Artificial intelligence meets body sense: task-driven neural networks reveal computational principles of the proprioceptive pathway

nature.com
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What makes humans similar or different to AI? In a new study, led by @florianmahner and @lukas_mut and w/ @umuguc, we took a deep look at the factors underlying their representational alignment, with surprising results. arxiv.org/abs/2406.19087 🧵

Dimensions underlying the representational alignment of deep...

Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal both in computational cognitive neuroscience and machine learning, promising a...

arxiv.org
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Super excited to share our recent work on privileged representational axes in biological and artificial neural networks! w/ @Nancy_Kanwisher @JoshHMcDermott @ItsNeuronal biorxiv.org/content/10.1101/…

Privileged representational axes in biological and artificial neural networks

How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings (or...

biorxiv.org
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Katharina Dobs@KathaDobs12 Apr 2024
🚨 Deadline is coming up soon. If you're passionate about working at the intersection of #AI, #neuroscience, and #vision, we'd love to have you join our lab as a #postdoc! 👇
Katharina Dobs@KathaDobs21 Mar 2024
I'm looking for a postdoc to explore the exciting intersection of #AI #neuroscience and #vision in our @ERC_Research-funded #DEEPFUNC project. Like to join our lab? Apply by April 15! uni-giessen.de/karriere/stel…. Please RT to help spread the word.
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I'm soon going to be looking for a senior postdoc to join my lab who is interested in transitioning to independence and in building their own group, with a strong background in vision science, cognitive computational neuroscience or visual neuroscience. Please RT 1/3
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Modeling Biological Face Recognition with Deep Convolutional Neural Networks @mitpress @mitpress dlvr.it/Svvqlv

Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Abstract. Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break...

direct.mit.edu
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How does our brain enable us to make sense of our visual world? 🗺👀🧠 Excited to share our latest work with @Chris_I_Baker and @martin_hebart : “Distributed representations of behaviorally relevant object dimensions in the human visual system” biorxiv.org/content/10.1101/… 🧵 1/n

Distributed representations of behaviorally relevant object dimensions in the human visual system

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorizat...

biorxiv.org
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Only one week left to apply! Come and work with us!
Martin Hebart@martin_hebart7 Jul 2023
I am looking for two postdocs to join us at Giessen University @jlugiessen, in collab. with the Max Planck Institute @MPI_CBS in Leipzig, funded by @ERC_Research & @ProLOEWE. We have exciting projects lined up but also offer a lot of freedom with implementing own ideas!👇 pls RT!
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Katharina Dobs@KathaDobs1 Aug 2023
It's been some time, but our paper is finally out in @PNASNews. 🥳 We found that deep neural networks optimized for face recognition (but not for face detection or object categorization) mirror long-known behavioral phenomena observed in face perception. pnas.org/doi/10.1073/pnas.22…
Katharina Dobs@KathaDobs28 Nov 2022
New preprint with @JoanneYuan3, @martinezjulioa_, @Nancy_Kanwisher! Using deep convolutional neural networks to test why human face recognition works the way it does. 🧵 biorxiv.org/cgi/content/shor…
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Lenny van Dyck retweeted
Martin Hebart@martin_hebart7 Jul 2023
I am looking for two postdocs to join us at Giessen University @jlugiessen, in collab. with the Max Planck Institute @MPI_CBS in Leipzig, funded by @ERC_Research & @ProLOEWE. We have exciting projects lined up but also offer a lot of freedom with implementing own ideas!👇 pls RT!
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Tim Kietzmann@TimKietzmann15 Mar 2023
🚨ERC Job Alert 🚨 Are you looking for a PhD position? Are you into AI and/or computational/cognitive neuroscience? Then consider becoming an ERC funded graduate student with us: ikw.uni-osnabrueck.de/filead… I am biased, but I think this is a great opportunity. 🧵
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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/figshare.pl…
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