Published in Proceedings of the National Academy of Sciences (PNAS), 2021
Recommended citation: Mehrer, Spoerer, Jones, Kriegeskorte, Kietzmann. (2021). "Ecoset: an ecologically more valid visual diet for deep learning yields better models of human high-level visual cortex." PNAS.
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Published in Journal of Cognitive Neuroscience, 2021
Recommended citation: Storrs, Kietzmann, Walther, Mehrer, Kriegeskorte. (2021). "Diverse deep neural networks all predict human IT well, after training and fitting." Journal of Cognitive Neuroscience.
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Published in Nature Communications, 2020
Recommended citation: Mehrer, Spoerer, Jones, Kriegeskorte, Kietzmann. (2020). "Individual differences among deep neural network models." Nature Communications.
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Published in PLOS Computational Biology, 2020
Recommended citation: Spoerer, Kietzmann, Mehrer, Charest, Kriegeskorte. (2020). "Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision." PLOS Computational Biology.
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Published in International Conference on Learning Representations (ICLR), 2026
Recommended citation: Mehrer, Lönnqvist, Mitola, Gökce, Papale, Schrimpf. (2026). "Model-guided microstimulation steers primate visual behavior." ICLR 2026.
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Published in International Conference on Learning Representations (ICLR), 2026
Recommended citation: Honarmand, Sharma, Al-Khamissi, Mehrer*, Schrimpf* (*shared last authorship). (2026). "Inducing dyslexia in vision language models." ICLR 2026.
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Published in Cognitive Computational Neuroscience (CCN) Conference (preprint), 2026
Recommended citation: Al-Khamissi*, Mehrer*, Marinov, Abdelaal, Gokce, Schrimpf (*shared first authorship). (2026). "Discovering functionally selective brain regions with deep topographic multimodal models." CCN 2026.
Published in International Conference on Learning Representations (ICLR) — Top 2% Oral, 2025
Recommended citation: Rathi*, Mehrer*, AlKhamissi, Binhuraib, Blauch, Schrimpf (*shared first authorship). (2025). "TopoLM: brain-like spatio-functional organization in a topographic language model." ICLR 2025.
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Published in BabyLM Challenge at Conference on Computational Natural Language Learning (CoNLL), 2024
Recommended citation: AlKhamissi, Tang, Gokce, Mehrer*, Schrimpf* (*shared last authorship). (2024). "Dreaming out loud: A self-synthesis approach for training vision-language models with developmentally plausible data." CoNLL 2024.
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