Research map / Learning theory and algorithms / Prediction and decision losses
Fast Active Set Methods for Online Spike Inference from Calcium Imaging
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use OASIS instead of generic convex solvers for AR(1) with given lambda | spike inference · simulated AR(1), T=3000 | computation time | Helped |
| use OASIS for AR(2) processes | spike inference · simulated AR(2) | computation time | Helped |
| use OASIS on real GCaMP6s data | spike inference · GCaMP6s 60 Hz | time per trace: 2.37 ± 0.12 s -> 0.101 ± 0.005 s | Helped |
| use OASIS instead of ECOS on zebrafish data | spike inference · 10,000 traces | total time: 2,818.1 s -> 81.5 s | Helped |
| use hard noise constraint instead of given lambda (ablation) | OASIS · simulated AR(1) | computation time | Hurt |
| optimize gamma iteratively instead of estimating from autocorrelation (ablation) | OASIS · inhomogeneous Poisson simulation | correlation with true spikes: 0.773 -> 0.869 | Helped |
| warm start active set after lambda update (ablation) | OASIS · hyperparameter optimization | computational cost | Helped |
| increase trace length from 3,000 to 30,000 frames | OASIS vs convex solvers · simulated traces | computation time | No clear effect |
| use OASIS online instead of convex solvers | spike inference · memory | RAM | Helped |
Closest papers by meaning
- SpikeSSL: A Universal Spike Inference Framework with Dynamics-Informed State-Space Layers (NeurIPS 2026 · similarity 0.89)
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- ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets (NeurIPS 2024 · similarity 0.85)
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- Active learning of neural population dynamics using two-photon holographic optogenetics (NeurIPS 2024 · similarity 0.85)
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