OmniMouse brain model scales with data, not size

Researchers have developed OmniMouse, a multi-modal, multi-task model trained on over 150 billion neural tokens from a mouse's visual cortex. This model demonstrates state-of-the-art performance in neural prediction, behavioral decoding, and neural forecasting, outperforming specialized baselines. Unlike typical AI scaling trends where model size is the primary driver, OmniMouse's performance scales reliably with data, but gains from increasing model size saturate, suggesting brain modeling remains data-limited. AI

IMPACT Suggests brain modeling remains data-limited, contrasting with typical AI scaling trends where model size is primary.

RANK_REASON The cluster describes a new research paper detailing a novel model and its scaling properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• house mouse
• Hugging Face
• Konstantin F Willeke
• Language Models
• Natural Movies Evoke Spike Trains with Low Spike Time Variability in Cat Primary Visual Cortex
• OmniMouse
• Visual Cortex

• paper
• model release

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