LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Authors: Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A Hamprecht
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, Le Mo NADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations. and 4 EXPERIMENTS AND RESULTS |
| Researcher Affiliation | Academia | 1Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany 2Institute for Anatomy and Cell Biology, Heidelberg University, Germany 3Institute of Physiology and Pathophysiology, Heidelberg University, Germany 4Dept. Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany |
| Pseudocode | Yes | Algorithm 1: The Le Mo NADe algorithm |
| Open Source Code | Yes | A Py Torch implementation of the proposed method is released at https://github.com/EKirschbaum/Le Mo NADe. |
| Open Datasets | No | The paper describes generating synthetic datasets using a procedure analogous to cited works (Diego et al., 2013; Diego & Hamprecht, 2013), and creating real datasets from organotypic hippocampal slice cultures. However, it does not provide concrete access (link, DOI, repository) to these specific generated or collected datasets. |
| Dataset Splits | No | The paper describes experiments on synthetically generated datasets and real datasets but does not explicitly provide details about train/validation/test splits, their percentages, or how they were partitioned for reproducibility. |
| Hardware Specification | Yes | The analysis of the datasets took less than two hours on a Ti 1080 GPU. |
| Software Dependencies | No | The paper mentions a 'Py Torch implementation' but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | Table 3 shows the parameter settings used for the experiments shown in the paper. |