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 and the companion website.

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.
• Training and evaluation: How to train and evaluate forecasting methods on ZAPBench in a framework agnostic way.
• Metrics: Explains how to load predictions made by the methods reported in the paper for additional analyses, e.g., to compute custom metrics.
• Interactive time-series forecasting: Shows how to run a jax time-series forecasting model interactively.

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.

License

Apache 2.0

This is not an officially supported Google product.