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A rotation-equivariant convolutional neural network model of primary visual cortex
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Share weights across orientations using rotation equivariance | CNN for mouse V1 response prediction · Mouse V1 natural-image test set | Pearson correlation | Helped |
| Add sparsity regularization to feature weights (ablation) | Rotation-equivariant CNN · Mouse V1 natural-image test set | Pearson correlation | Helped |
| Fit the shared core without 20% of neurons and train only their readouts | Rotation-equivariant CNN · Held-out neurons, mouse V1 natural-image test set | Pearson correlation: 0.46 vs. 0.47 | No clear effect |
Closest papers by meaning
- Rotation-invariant clustering of neuronal responses in primary visual cortex (ICLR 2020 · similarity 0.94)
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- Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. (NeurIPS 2024 · similarity 0.86)
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