Sparse convolutional coding for neuronal assembly detection
Authors: Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Testing of our algorithm on synthetically generated datasets shows that it outperforms established methods and accurately identifies the temporal structure of embedded assemblies, even when these contain overlapping neurons or when strong background noise is present. Moreover, exploratory analysis of experimental datasets from hippocampal slices and cortical neuron cultures have provided promising results. 4.1 Synthetic data, 4.2 Real data |
| Researcher Affiliation | Collaboration | 1Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, Germany 2Institute of Physiology and Pathophysiology, Heidelberg, Germany 3National Institute on Drug Abuse, Baltimore, USA 4Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany 5Dept. Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany 6Robert Bosch Gmb H, Hildesheim, Germany |
| Pseudocode | No | The paper describes the optimization algorithm steps in text but does not include structured pseudocode or an algorithm block in the main body. It states that "All details of the algorithm are outlined in the supplementary material for this paper." (page 3). |
| Open Source Code | Yes | All code for the proposed method is available at: https://github.com/sccfnad/Sparse-convolutional-coding-for-neuronal-assembly-detection |
| Open Datasets | Yes | Testing of our algorithm on synthetically generated datasets shows that it outperforms established methods... For further analysis, various datasets consisting of fifty neurons observed over one thousand time frames were created. Details on the generation of these datasets can be found in the supplementary material. In vitro hippocampal CA1 region data. We analyzed spike trains of 91 cells from the hippocampal CA1 region recorded at high temporal and multiple single cell resolution using CA2+ imaging. The acute mouse hippocampal slices were recorded in a so-called interface chamber [47]. In vitro cortical neuron culture data. Primary cortical neurons were prepared from E15 embryos of Sprague Dawley rats as described in [51] and approved by the NIH Animal Care and Usage Committee. |
| Dataset Splits | No | The paper describes generating synthetic datasets for testing and analyzing real-world datasets but does not provide explicit training/validation/test dataset splits with percentages, sample counts, or specific pre-defined split methodologies. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware specifications (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or library names with versions). |
| Experiment Setup | Yes | Table 1: Experimental parameters. We show the used maximal number of assemblies, maximal motif length in frames, ℓ1 penalty value β, and number of runs of the algorithm with different initializations for the performed experiments on synthetic and real datasets. We also display the estimated threshold T used for distinguishing between real and spurious motifs. |