@__init_self profile picture
Victoria Bosch @__init_self

neuromantic - ML and cognitive computational neuroscience - PhD student at Kietzmann Lab, Osnabrück University.

Joined October 2021
  • Tweets 133
  • Following 524
  • Followers 431
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Victoria Bosch@__init_self
3 Nov 2025
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
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MedARC@MedARC_AI
May 19
We're excited to announce we're starting a Journal Club. And our first meeting is scheduled for tomorrow! @__init_self will present her work, CorText: Brain-Language Fusion Enables Interactive Neural Readout and In-Silico Experimentation Tomorrow at 10:15am ET, join Discord!
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Tim Kietzmann@TimKietzmann
May 12
Looking for robust AI vision? Then this NeuroAI paper may be for you 👇 Open access, open code, open weights.
Zejin Lu | 陆泽金@lu_zejin
May 11
Now out in Nature Machine Intelligence @NatMachIntell “Adopting a human developmental visual diet yields robust and shape-based AI vision”: nature.com/articles/s42256-0…. A wonderful case where brain inspiration improved AI solutions. With @martisamuser, Radek Cichy and @TimKietzmann.
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Daniel Anthes@AnthesDaniel
May 11
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

The illusory simplicity of the feedforward pass: evidence for the...

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...

arxiv.org
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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
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more replies
We are convinced that these results mark a shift from static neural decoding toward interactive, generative brain-language interfaces. Preprint: arxiv.org/abs/2509.23941

Brain-language fusion enables interactive neural readout and...

Large language models (LLMs) have revolutionized human-machine interaction, and have been extended by embedding diverse modalities such as images into a shared language space. Yet, neural decoding...

arxiv.org
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Very excited to finally share this project with the world. Thanks to all co-authors! 🦾 @AnthesDaniel @AdrienDoerig @martisamuser @konigpeter @TimKietzmann /fin
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Joséphine Raugel@JRaugel
1 Sep 2025
Very pleased to share our latest study!
Jean-Rémi King@JeanRemiKing
1 Sep 2025
Can AI help understand how the brain learns to see the world? Our latest study, led by @JRaugel from FAIR at @AIatMeta and @ENS_ULM, is now out! 📄 arxiv.org/pdf/2508.18226 🧵 A thread:
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Adrien Doerig@AdrienDoerig
7 Aug 2025
🚨 Finally out in Nature Machine Intelligence!! "Visual representations in the human brain are aligned with large language models" nature.com/articles/s42256-0…

High-level visual representations in the human brain are aligned with large language models

Nature Machine Intelligence - Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain...

nature.com
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Yuki Kamitani@ykamit
11 Jul 2025
Our new study in @NatComputSci, led by Haibao Wang, presents a neural code converter aligning brain activity across individuals & scanners without shared stimuli by minimizing content loss, paving the way for scalable decoding and cross-site data analysis. nature.com/articles/s43588-0…

Inter-individual and inter-site neural code conversion without shared stimuli

Nature Computational Science - A neural code conversion method is introduced using deep neural network representations to align brain data across individuals without shared stimuli. The approach...

nature.com
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Tim Kietzmann@TimKietzmann
8 Jul 2025
Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @lu_zejin @martisamuser and Radoslaw Cichy arxiv.org/abs/2507.03168

Adopting a human developmental visual diet yields robust,...

Despite years of research and the dramatic scaling of artificial intelligence (AI) systems, a striking misalignment between artificial and human vision persists. Contrary to humans, AI relies...

arxiv.org
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Wilka Carvalho
@cogscikid
16 Jun 2025
Excited to share project specifying a research direction I think will be particularly fruitful for theory-driven cognitive science that aims to explain natural behavior! We're calling this direction "Naturalistic Computational Cognitive Science"
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Mona
@dyot_meet_mat
27 Jun 2025
"The Garden of Forking Paths" By: Opus4🤖
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Martin Hebart@martin_hebart
23 Jun 2025
Very happy to announce that our paper comparing dimensions in human and DNN representations is now out in @NatMachIntell nature.com/articles/s42256-0…

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...

nature.com
Martin Hebart@martin_hebart
28 Jun 2024
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 🧵
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The vOICe vision BCI 🧠🇪🇺@seeingwithsound
22 Jun 2025
Beyond reorganization: Intrinsic cortical hierarchies constrain experience-dependent plasticity in sensory-deprived humans biorxiv.org/content/10.1101/… "we confirm that auditory and speech related features are redirected to deprived visual cortices in blind individuals"; #neuroscience
Feature-specific recruitment of deprived sensory cortices.

ALT Feature-specific recruitment of deprived sensory cortices.

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