Mouse Visual Cortex Predicts Higher-Level Features
Heilbron and de Lange (published January 20, 2026) used deep generative modeling to quantify spatial predictability of receptive-field image patches and compared these estimates to high-density recordings from the Allen Institute Visual Coding Dataset across mouse visual cortex. They report that more predictable patches evoke weaker responses, sensitivity is strongest for higher-level features (even in V1), is elevated in superficial layers, and reflects long-term priors.
Key Points
- 1Finds weaker neural responses to more predictable image patches across mouse visual cortex
- 2Shows neurons are primarily sensitive to predictability of higher-level features rather than low-level edges
- 3Indicates spatial prediction relies on long-term priors and informs predictive self-supervised learning architectures
Scoring Rationale
Strong, novel evidence linking generative-model spatial predictability to cortical responses; scope limited to mouse visual system.
Sources
Public references used for this report.
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