OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
Cadena, George H
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A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
No correlation exists between CNNs' Brain-Score alignment with the visual system and the perceptual content of their Gram-matrix texture representations.
citing papers explorer
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OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
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Zero-shot World Models Are Developmentally Efficient Learners
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
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Perceptual misalignment of texture representations in convolutional neural networks
No correlation exists between CNNs' Brain-Score alignment with the visual system and the perceptual content of their Gram-matrix texture representations.