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..
Flexible statistical inference for mechanistic models of neural dynamics
Authors: Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On synthetic data, our approach efficiently estimates posterior distributions and recovers ground-truth parameters. On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, which yield model-predicted voltage traces that accurately match empirical data. |
| Researcher Affiliation | Academia | 1 research center caesar, an associate of the Max Planck Society, Bonn, Germany 2 Mathematical Institute, University of Bonn, Bonn, Germany |
| Pseudocode | No | The paper describes algorithms and methods in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/mackelab/delfi |
| Open Datasets | Yes | We also applied the approach to in vitro recordings from the mouse visual cortex (see Appendix D.4, Fig. 3E-G). ... Inference over 12 parameters for cell 464212183. |
| Dataset Splits | No | The paper focuses on parameter inference for models of neural dynamics and does not specify traditional train/validation/test dataset splits for the primary experimental data used in this context. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like NEURON and Blue Py Opt, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper describes the architecture (MDN, RNN, GRUs) and general training strategy (sequential, importance-weighted loss, variational inference) but lacks specific numerical hyperparameters like learning rates, batch sizes, or optimizer details for the experimental setup. |