https://github.com/google-research/zapbench

The Zebrafish Activity Prediction Benchmark measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain.
https://github.com/google-research/zapbench

benchmark calcium-imaging forecasting light-sheet-microcopy machine-learning neuroscience time-series

Last synced: 23 days ago
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The Zebrafish Activity Prediction Benchmark measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain.

• Host: GitHub
• URL: https://github.com/google-research/zapbench
• Owner: google-research
• License: apache-2.0
• Created: 2025-01-29T18:35:42.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2026-04-20T10:57:22.000Z (2 months ago)
• Last Synced: 2026-04-20T12:41:39.826Z (2 months ago)
• Topics: benchmark, calcium-imaging, forecasting, light-sheet-microcopy, machine-learning, neuroscience, time-series
• Language: Python
• Homepage: https://google-research.github.io/zapbench
• Size: 152 KB
• Stars: 70
• Watchers: 5
• Forks: 13
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

          
# ZAPBench ⚡

The Zebrafish Activity Prediction Benchmark (ZAPBench) measures progress on the

 problem of predicting cellular-resolution neural activity throughout an entire

 vertebrate brain. For more information, refer to [our ICLR paper](https://openreview.net/pdf?id=oCHsDpyawq) and the [companion website](https://google-research.github.io/zapbench).

## Getting started

To get started with ZAPBench, we provide tutorial-style notebooks in the `colabs/` directory:

- **Datasets:** Overview of various datasets we released and how to access them. [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/zapbench/blob/main/colabs/datasets.ipynb)

- **Training and evaluation:** How to train and evaluate forecasting methods on ZAPBench in a framework agnostic way. [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/zapbench/blob/main/colabs/train_and_evaluate.ipynb)

- **Metrics:** Explains how to load predictions made by the methods reported in the paper for additional analyses, e.g., to compute custom metrics. [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/zapbench/blob/main/colabs/metrics.ipynb)

- **Interactive time-series forecasting:** Shows how to run a `jax` time-series forecasting model interactively. [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/zapbench/blob/main/colabs/ts_forecasting_interactive.ipynb)

## Contents

In addition, this repository contains:

- Code for the forecasting models used in the paper, implemented in `jax`, in the `zapbench/models/` subdirectory.

- Scripts and configs to train and evaluate time-series and video forecasting models, in `zapbench/ts_forecasting/` and `zapbench/video_forecasting/`, respectively. The READMEs in those subdirectories contain further usage instructions.

- Config for alignment and normalization pipeline of the raw data in `processing/alignment_and_normalization.gin`; see file header for usage.

- Notebook demonstrating how to load the FFN checkpoint used for segmentation in `processing/ffn_inference.ipynb`.

- Notebook loading and plotting raw stimulus time-series in `processing/stimuli.ipynb`.

- A WebGL-viewer for calcium fluorescence data in `fluroglancer/`.

## Datasets

[Further information on associated datasets](http://zapbench-release.storage.googleapis.com/volumes/README.html).

## License

Apache 2.0

*This is not an officially supported Google product.*