Robust Estimation of Neural Signals in Calcium Imaging
Authors: Hakan Inan, Murat A. Erdogdu, Mark Schnitzer
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superiority of our robust estimation approach over existing methods on both simulated and real datasets. |
| Researcher Affiliation | Collaboration | Hakan Inan 1 inanh@stanford.edu Murat A. Erdogdu 2,3 erdogdu@cs.toronto.edu Mark J. Schnitzer 1,4 mschnitz@stanford.edu 1Stanford University 2Microsoft Research 3Vector Institute 4Howard Hughes Medical Institute |
| Pseudocode | Yes | Algorithm 1 Fast Solver for one-sided Huber Loss; Algorithm 2 Tractable and Robust Automated Cell Extraction |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For simulated movies, we use a field of view of size 50 by 50 pixels, and produce data with 1000 time frames. [...] We put EXTRACT to test in this data regime, using an imaging dataset recorded from the dorsal CA1 region of the mouse hippocampus [17], an area known to have high cell density. [17] Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer. Long-term dynamics of ca1 hippocampal place codes. Nature neuroscience, 16(3):264 266, 2013. |
| Dataset Splits | No | The paper describes initializations and testing conditions but does not provide specific train/validation/test dataset split information (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper mentions 'GPU implementation' but does not specify any particular GPU models, CPU types, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the experiments. |
| Experiment Setup | Yes | For our fixed-point solver, we use κ = 1. [...] A range of [0.5, 1] times the standard deviation of the normally distributed noise is reasonable for κ for most practices. [...] We simulate the fluorescence traces using a Poisson process with rate 0.01 convolved with an exponential kernel with a time constant of 10 frames. [...] We initialize the algorithms with 4 different fractions of ground truth cells: X = {0.2, 0.4, 0.6, 0.8}. We carry out 20 experiments for each X, and we perform a 3 alternating estimation iterations for each algorithm. [...] We choose to use the greedy initializer of CNMF to eliminate any competitive advantage EXTRACT might have due to using its native initializer. |