Fast Active Set Methods for Online Spike Inference from Calcium Imaging
Authors: Johannes Friedrich, Liam Paninski
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We generalize PAVA to derive an Online Active Set method to Infer Spikes (OASIS) that yields speed-ups in processing time by at least one order of magnitude compared to interior point methods on both simulated and real data. ... Figures 4D,E report the computation time ( SEM) averaged over all 20 traces and ten runs per trace on a Mac Book Pro with Intel Core i5 2.7 GHz CPU. |
| Researcher Affiliation | Academia | Johannes Friedrich1,2, Liam Paninski1 1Grossman Center and Department of Statistics, Columbia University, New York, NY 2Janelia Research Campus, Ashburn, VA j.friedrich@columbia.edu, liam@stat.columbia.edu |
| Pseudocode | Yes | Algorithm 1 Fast online deconvolution algorithm for AR(1) processes with positive jumps |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | Running OASIS with the hard noise constraint and p = 2 on the GCa MP6s dataset collected at 60 Hz from [29] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | Figures 4D,E report the computation time ( SEM) averaged over all 20 traces and ten runs per trace on a Mac Book Pro with Intel Core i5 2.7 GHz CPU. |
| Software Dependencies | No | The paper mentions software like 'CVXPY', 'ECOS', 'MOSEK', 'SCS', and 'GUROBI' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We used γ = 0.95, σ = 0.3 for the AR(1) model and γ1 = 1.7, γ2 = 0.712, σ = 1 for the AR(2) model. |