Research map / Point clouds / Point cloud classification
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply Poisson denoising to localization images before motion estimation (ablation) | Decentralized registration on Neuropixels 2.0 recording · NP2.0 recording with induced motion | motion estimate jitter | Helped |
| Use proposed 3D localization instead of Hurwitz et al. and center of mass for registration | Motion correction pipeline · NP2.0 recording with induced motion | average correlation, RMSE, image entropy | Helped |
| Add amplitude-scale feature alpha to x,z location features for clustering (ablation) | GMM waveform clustering · NP2.0 spikes in selected z-boxes | waveform cluster separation (qualitative) | Helped |
| Add neural-net denoising and collision suppression before localization (ablation) | Spike localization · NP2.0 spikes | localization quality (qualitative) | Helped |
Closest papers by meaning
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- Point-to-Spike Residual Learning for Energy-Efficient 3D Point Cloud Classification (AAAI 2024 · similarity 0.85)
- Nonlinear dynamics of localization in neural receptive fields (NeurIPS 2024 · similarity 0.84)
- Efficient 3D Recognition with Event-driven Spike Sparse Convolution (AAAI 2025 · similarity 0.84)
- Spike Distance Function as a Learning Objective for Spike Prediction (ICML 2024 · similarity 0.84)
- Scalable inference of functional neural connectivity at submillisecond timescales (NeurIPS 2025 · similarity 0.84)
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