Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mesoscopic modeling of hidden spiking neurons
Authors: Shuqi Wang, Valentin Schmutz, Guillaume Bellec, Wulfram Gerstner
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show, on synthetic spike trains, that a few observed neurons are sufficient for neu LVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking optogenetic stimulation. 5 Experimental results |
| Researcher Affiliation | Academia | Laboratory of Computational Neuroscience École polytechnique fédérale de Lausanne (EPFL) |
| Pseudocode | No | The paper describes algorithms such as the Baum-Viterbi algorithm but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Our implementation of the algorithm is openly available at https://github.com/EPFL-LCN/neuLVM. |
| Open Datasets | No | To build a spiking benchmark dataset, we randomly selected 9 neurons 3 neurons from each of the three populations and considered the spike trains of these neurons as the observed data. The paper generates synthetic data and does not provide a link or specific citation to make it publicly available. |
| Dataset Splits | No | The paper generates synthetic data and a benchmark dataset but does not explicitly provide details on how the data was split into training, validation, and test sets with specific percentages or counts. |
| 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 to run the experiments. |
| Software Dependencies | No | The paper states "Our implementation of the algorithm is openly available at https://github.com/EPFL-LCN/neuLVM" but does not specify any particular software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or other libraries). |
| Experiment Setup | No | The paper describes some network parameters and trial durations (e.g., 'J = 60.32 m V', '10 seconds recordings') but does not provide specific hyperparameter values (like learning rate, batch size, epochs for training neu LVM) or comprehensive system-level training settings in a dedicated section. |