Research map / Video / Video segmentation and tracking
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace linear autoencoder with convolutional autoencoder for video compression (ablation) | BehaveNet · WFCI, NP, NP-zoom | reconstruction MSE per pixel | Helped |
| add Bayesian decoder with ARHMM prior on top of feedforward decoder (ablation) | neural decoding of CAE latents · WFCI, NP, NP-zoom | latent MSE | Helped |
| add Bayesian decoder with ARHMM prior on top of feedforward decoder (ablation) | neural decoding of discrete states · WFCI, NP, NP-zoom | state accuracy | No clear effect |
| use feedforward decoder instead of simple linear decoder (ablation) | neural decoding of CAE latents · WFCI, NP, NP-zoom | latent MSE | Helped |
| decode neural activity into CAE latents versus mean baseline, cropped face video versus full video | Bayesian decoder · NP vs NP-zoom | MSE improvement over baseline: 37% (NP) vs 67% (NP-zoom) | Mixed |
Closest papers by meaning
- Learning Disentangled Behavior Embeddings (NeurIPS 2021 · similarity 0.90)
- Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors (NeurIPS 2025 · similarity 0.87)
- Neural Encoding and Decoding at Scale (ICML 2025 · similarity 0.86)
- Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models (NeurIPS 2024 · similarity 0.86)
- Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity (NeurIPS 2025 · similarity 0.86)
- BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation (ICLR 2025 · similarity 0.86)
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