Extracting Latent Structure From Multiple Interacting Neural Populations
Authors: Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then applied these methods to populations of neurons recorded simultaneously in visual areas V1 and V2, and found that g LARA provides a better description of the recordings than p CCA. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Carnegie Mellon University 2Department of Electrical and Computer Engineering, Instituto Superior T ecnico 3Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown 4Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine 5Department of Biomedical Engineering, Carnegie Mellon University |
| Pseudocode | No | The paper describes the mathematical models and the Expectation-Maximization (EM) algorithm steps for parameter estimation using equations and textual descriptions, but it does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper describes the dataset used as 'multi-electrode recordings performed simultaneously in visual area 1 (V1) and visual area 2 (V2) of an anaesthesised monkey'. This appears to be a proprietary dataset collected by the authors, and no information (link, DOI, repository, or citation to a public source) is provided for accessing it. |
| Dataset Splits | Yes | For model comparison, we performed 4-fold cross-validation, splitting the data into four non-overlapping test folds with 250 trials each. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or computing cluster specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers that would be required to replicate the experiments (e.g., specific programming languages, libraries, or scientific computing environments with versions). |
| Experiment Setup | Yes | We used 1.23s of data in each trial, from 50ms after stimulus onset until the end of the trial, and proceeded to bin the observed spikes with a 5ms window. For model comparison, we performed 4-fold cross-validation, splitting the data into four non-overlapping test folds with 250 trials each. AR-p CCA required a latent dimensionality of p = 70, while g LARA peaked for a joint latent dimensionality of 65 (p1 = 50 and p2 = 15). |