Sparse Space-Time Deconvolution for Calcium Image Analysis

Authors: Ferran Diego Andilla, Fred A. Hamprecht

NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on real and synthetic data demonstrate the viability of the proposed method.
Researcher Affiliation Academia Ferran Diego Fred A. Hamprecht Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR) University of Heidelberg, Heidelberg 69115, Germany {ferran.diego,fred.hamprecht}@iwr.uni-heidelberg.de
Pseudocode No The paper presents mathematical formulations of the optimization problem and describes the optimization strategy in text, but it does not include a pseudocode block or a clearly labeled algorithm.
Open Source Code No The paper does not provide any statement about releasing the source code or a link to a code repository for the described methodology.
Open Datasets No The paper describes how synthetic data was created following procedures in cited works ([24, 5], [4, 12, 13]) and mentions using real-world data from a cited source ([7]), but it does not provide direct access information (e.g., URL, DOI, specific repository) for the exact datasets used in their experiments.
Dataset Splits No The paper describes the generation parameters for synthetic data but does not explicitly provide information on how the dataset was split into training, validation, and test sets (e.g., percentages, sample counts, or specific predefined splits).
Hardware Specification No The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications).
Software Dependencies No The paper does not specify any software dependencies with version numbers.
Experiment Setup Yes We used J = L = 2, K = 200, F = 200 and H = 31, 15 for the artificial and real data, respectively.