OnACID: Online Analysis of Calcium Imaging Data in Real Time
Authors: Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our algorithm on two large scale experimental datasets, benchmark its performance on manually annotated data, and show that it outperforms a popular offline approach. (Abstract) ... Benchmarking on simulated data ... Application to in vivo 2p mouse hippocampal data ... Benchmarking against offline processing and manual annotations ... Application to in vivo 2p mouse parietal cortex data. |
| Researcher Affiliation | Academia | Flatiron Institute, New York, NY 10010 Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 Columbia University, New York, NY 10027 |
| Pseudocode | Yes | The whole online procedure is described in Algorithm 1; the supplement includes pseudocode description of all the referenced routines. (Page 4) ... Algorithm 1 ONACID (Page 4) |
| Open Source Code | Yes | Software: On ACID is implemented in Python and is available at https://github.com/simonsfoundation/caiman as part of the Ca Im An package [13]. (Page 4) |
| Open Datasets | No | The paper describes using 'simulated data' and 'real 2p calcium imaging datasets' for experiments. While it mentions the characteristics of these datasets (e.g., '2000 frame dataset', '90K frames', '116,000 frame dataset'), it does not provide any specific links, DOIs, repository names, or formal citations for public access to these datasets. The acknowledgments state, 'We thank Sue Ann Koay, Jeff Gauthier and David Tank (Princeton University) for sharing their cortex and hippocampal data with us,' which suggests the data was shared privately rather than being publicly available. |
| Dataset Splits | No | The paper describes initialization periods (e.g., 'On ACID was initialized on the first 500 frames', 'initialized on the first 1000 frames', 'The first 3000 frames were used for initialization') for its online algorithm. However, it does not specify explicit training, validation, and test splits with percentages, sample counts, or references to predefined standard splits for the entire datasets used in the experiments. |
| Hardware Specification | No | The paper mentions 'a typical 2p experiment (512 x 512 pixel wide FOV imaged at 30Hz)' which describes the data acquisition setup. However, it does not specify any hardware details (e.g., CPU models, GPU models, memory, or cloud computing instances) used for running the computational experiments or analyses described in the paper. |
| Software Dependencies | No | The paper states: 'On ACID is implemented in Python and is available at https://github.com/simonsfoundation/caiman as part of the Ca Im An package [13].' While it mentions Python and the CaImAn package, it does not provide specific version numbers for either, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | On ACID was initialized on the first 500 frames. (Page 4) ... The online algorithm was initialized on the first 1000 frames of the dataset. (Page 5) ... The first 3000 frames were used for initialization. (Page 6) ... For both the datasets presented above, the analysis was done using the same space correlation threshold θs = 0.9. (Page 6) ... We approach the problem by introducing a buffer that contains the last lb instances of the residual signal rt, where lb is a reasonably small number, e.g., lb = 100. (Page 4) ... We use p = 1 here, but can extend to p = 2 to incorporate the indicator rise time [11]. (Page 3) ... The calcium dynamics is modeled with a stable autoregressive process of order p. (Page 3) |