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 | Conference PDF | Archive PDF | Plain Text | 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. |