@__init_selfi
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neuromantic - ML and cognitive computational neuroscience - PhD candidate at Kietzmann Lab, Osnabrück University. 🦋
Joined October 2021
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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 🧠💬
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Victoria Bosch retweeted
We’re excited to announce DiG-bench, a new benchmark for discovery!
Over the last few weeks we’ve been testing frontier AI models on our novel discovery games and seeing how they score. Each game is a text-based environment, so they probe discovery capabilities in the natural domain of language models, rather than requiring additional, potentially confounding, visual understanding.
TL;DR frontier models have improved a lot over the last few months. But they are still stumped by some surprisingly simple problems, even in their native text domain.
With
@cocosci_lab (@Princeton) @MITCoCoSci (@MIT) @SchmidhuberAI (@KAUST_News) @misovalko (@Inria) @tri_dao (@PrincetonCS) @RMBattleday @zebkDotCom @FraserGreenlee @akaijsa @ClareMaguire @TimMuller1 @kubicek_ales @physicscat0x7d @SukritSumant @thoughtchannel_
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Victoria Bosch retweeted
To learn how brains compute, we need to experimentally adjudicate among competing computational hypotheses. How do we do this in the age of complex neural network models? New review paper: “Making models disagree to learn how brains compute” with @TalGolanNeuro, @SchuettHeiko
Victoria Bosch retweeted
How can we design experiments that make models disagree? One section of our new @NatRevNeurosci Review with @KriegeskorteLab and @SchuettHeiko examines studies that used stimulus sets designed to elicit distinct predictions from competing models. Full text link at the end. 1/14
Are brains and artificial neural networks converging onto universal representations?
There is a seductive idea making the rounds in NeuroAI / ML: train systems well enough, and they'll converge on the same representation of reality (i.e. unique world model).
We have Thoughts™
Our proposal: To understand representational alignment between artificial neural networks and brains, don't look for the *one world model*, but use the toolbox of neuroconnectionism to map the space of ecological constraints that lead to systematic (mis)alignment instead.
Our article "The Umwelt Representation Hypothesis: rethinking Universality" is out now in
@TrendsCognSci , with my amazing colleagues:
@RowanSommers
@AdrienDoerig
@TimKietzmann
Many thanks to the journal and reviewers for their thoughtful feedback!
cell.com/trends/cognitive-sc…
Victoria Bosch retweeted
The Umwelt Representation Hypothesis: Rethinking Universality
Opinion by Victoria Bosch (@__init_self), Rowan Sommers, Adrien Doerig (@AdrienDoerig), & Tim Kietzmann (@TimKietzmann)
tinyurl.com/y7rty324
Victoria Bosch retweeted
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!
Victoria Bosch retweeted
Looking for robust AI vision? Then this NeuroAI paper may be for you 👇
Open access, open code, open weights.
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.
Victoria Bosch retweeted
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
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
Very excited to finally share this project with the world. Thanks to all co-authors! 🦾
@AnthesDaniel @AdrienDoerig @martisamuser @konigpeter @TimKietzmann
/fin