Clustered factor analysis of multineuronal spike data
Authors: Lars Buesing, Timothy A Machado, John P. Cunningham, Liam Paninski
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the merits of the proposed model by applying it to calcium-imaging data from spinal cord neurons, and we show that it uncovers meaningful clustering structure in the data. Here, we extend unstructured factor models by proposing a model that discovers subpopulations or groups of cells from the pool of recorded neurons. We tested the mix PLDS model on calcium imaging data obtained from an in vitro, neonatal mouse spinal cord that expressed the calcium indicator GCa MP3 in all motor neurons. |
| Researcher Affiliation | Academia | 1 Department of Statistics, Center for Theoretical Neuroscience & Grossman Center for the Statistics of Mind 2 Howard Hughes Medical Institute & Department of Neuroscience Columbia University, New York, NY {lars,cunningham,liam}@stat.columbia.edu |
| Pseudocode | No | The paper describes algorithms and procedures in textual form and through mathematical equations, but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures with structured pseudocode. |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper uses 'artificial data' and 'calcium imaging data obtained from an in vitro, neonatal mouse spinal cord'. While it describes these datasets, it does not provide concrete access information (e.g., links, DOIs, or citations to publicly available versions) for either dataset. |
| Dataset Splits | No | The paper describes generating artificial data ('We generate 35 random ground truth mix PLDS models... We sampled from each ground truth model a data set...') and using 'calcium imaging data' as input for evaluation. However, it does not specify training/validation/test splits, exact percentages, or sample counts needed to reproduce data partitioning, nor does it reference predefined splits with citations for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper mentions certain algorithms and methods (e.g., 'deconvolution algorithm [20]', 'L2 regularized generalized linear model estimation'), but it does not list specific software components or libraries with their version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | We generate 35 random ground truth mix PLDS models with M = 3, d1 = d2 = d3 = 2 and 20 observed neurons per cluster. We used a mix PLDS model with M = 2 groups with two latent dimensions each, i.e. d1 = d2 = 2. We imposed the non-negativity constraints C 0 on the loading matrix; these were found to be crucial for finding a meaningful clustering of the neurons, as discussed above. |