Research map / Architectures and efficiency / RNNs and normalization
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| reduce the CNN to three important subunits (1 OFF, 2 ON) found by attribution (ablation) | retina CNN model · omitted stimulus response | period-dependent shift in burst peak | Helped |
| use only one ON/OFF pathway pair (slow ON, fast OFF) from the CNN (ablation) | retina CNN model · latency coding | reproduction of latency shift with contrast | Helped |
| keep only cell types 2 and 3 across x positions (288 units reduced) (ablation) | retina CNN model · motion reversal | reproduction of burst with fixed latency | Helped |
| remove cell-type 2 inhibition (keep only cell type 3) (ablation) | reduced retina model · motion reversal | fixed latency of reversal response | Hurt |
| add direction-sensitive inhibition from second cell type to the excitatory cell type (ablation) | reduced retina model · motion anticipation | advance of response toward leading edge | Helped |
Closest papers by meaning
- Deep Learning Models of the Retinal Response to Natural Scenes (NeurIPS 2016 · similarity 0.93)
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- Natural image synthesis for the retina with variational information bottleneck representation (NeurIPS 2022 · similarity 0.90)
- A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains (NeurIPS 2023 · similarity 0.90)
- Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex (NeurIPS 2021 · similarity 0.89)
- Flexible Context-Driven Sensory Processing in Dynamical Vision Models (NeurIPS 2024 · similarity 0.87)
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