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.
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
jaxtime-series forecasting model interactively.
In addition, this repository contains:
- Code for the forecasting models used in the paper, implemented in
jax, in thezapbench/models/subdirectory. - Scripts and configs to train and evaluate time-series and video forecasting models, in
zapbench/ts_forecasting/andzapbench/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/.
Further information on associated datasets.
Apache 2.0
This is not an officially supported Google product.