Datasets:

mackelab
/

tsnpe_neurips

license: agpl-3.0
pretty_name: TSNPE NeurIPS 2022 - Results Data
tags:
  - simulation-based-inference
  - neuroscience
  - computational-biology
size_categories:
  - 1K<n<10K

TSNPE NeurIPS 2022 — Results Data

This dataset contains the trained inference networks, simulation outputs, and intermediate results used to reproduce the neuroscience figures (Fig. 5 — pyloric network, Fig. 6 — layer 5 pyramidal cell) of:

Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke. Truncated proposals for scalable and hassle-free simulation-based inference. NeurIPS 2022. https://openreview.net/forum?id=QW98XBAqNRa

It is the data companion to the code repository mackelab/tsnpe_neurips, and mirrors the directory structure that code expects under l5pc/results/. These files were previously stored via Git LFS directly in that repository; they now live here to keep the code repo's clone size and Git LFS bandwidth low.

l20_7/inference/ — two training runs for the layer 5 pyramidal cell (L5PC, Fig. 6) model: trained inference networks (inference.pkl), validation log-probabilities, held-out observations (xo.pkl), and TensorBoard logs (sbi-logs/) from the sbi training loop.
p31_4/multiround/ — multi-round inference results for the pyloric network (Fig. 5) across several training configurations (APT with/without transformation, TSNPE), one inference_r*.pkl per run/round.
p31_4/prior_predictives_energy_paper/ — prior predictive simulation outputs (all_circuit_parameters.pkl, all_simulation_outputs.pkl) used for the energy consumption analysis in the paper.
simulations_pickle/ — simulated parameter/observation pairs (simulations_theta_r*.pkl, simulations_x_r*.pkl) for the L5PC model, used to train the networks in l20_7/inference/ without re-running the simulator.

All files are Python pickles produced by the sbi toolbox and the code in the companion repository; there is no datasets-library loading script — load them directly with pickle/torch.load as consumed by the notebooks in mackelab/tsnpe_neurips.

Usage

pip install -U "huggingface_hub[cli]"
hf download mackelab/tsnpe_neurips --repo-type dataset --local-dir l5pc/results

Place the downloaded l5pc/results/ directory at the corresponding path inside a checkout of mackelab/tsnpe_neurips to reproduce Fig. 5 and Fig. 6 of the paper.

Citation

@inproceedings{
  deistler2022truncated,
  title={Truncated proposals for scalable and hassle-free simulation-based inference},
  author={Michael Deistler and Pedro J. Goncalves and Jakob H. Macke},
  booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
  year={2022},
  url={https://openreview.net/forum?id=QW98XBAqNRa}
}

License

AGPL-3.0, matching the code repository.

Contact

If you have questions, please reach out to michael.deistler@uni-tuebingen.de.

TSNPE NeurIPS 2022 — Results Data

Contents

Usage

Citation

License

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