@__init_self

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

Joined October 2021
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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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_ (1/5)
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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
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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
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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™
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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.
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The Umwelt Representation Hypothesis: Rethinking Universality Opinion by Victoria Bosch (@__init_self), Rowan Sommers, Adrien Doerig (@AdrienDoerig), & Tim Kietzmann (@TimKietzmann) tinyurl.com/y7rty324
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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!
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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.
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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
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