{"id":50533933,"url":"https://github.com/google-research/zapbench","last_synced_at":"2026-06-03T15:31:30.764Z","repository":{"id":286815074,"uuid":"924288757","full_name":"google-research/zapbench","owner":"google-research","description":"The Zebrafish Activity Prediction Benchmark measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain.","archived":false,"fork":false,"pushed_at":"2026-04-20T10:57:22.000Z","size":156,"stargazers_count":70,"open_issues_count":2,"forks_count":13,"subscribers_count":5,"default_branch":"main","last_synced_at":"2026-04-20T12:41:39.826Z","etag":null,"topics":["benchmark","calcium-imaging","forecasting","light-sheet-microcopy","machine-learning","neuroscience","time-series"],"latest_commit_sha":null,"homepage":"https://google-research.github.io/zapbench","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/google-research.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-01-29T18:35:42.000Z","updated_at":"2026-04-20T10:57:14.000Z","dependencies_parsed_at":"2025-06-06T19:29:54.621Z","dependency_job_id":"88e6d868-7686-4c47-8f1c-fb8668d87e60","html_url":"https://github.com/google-research/zapbench","commit_stats":null,"previous_names":["google-research/zapbench"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/google-research/zapbench","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fzapbench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fzapbench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fzapbench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fzapbench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/google-research","download_url":"https://codeload.github.com/google-research/zapbench/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fzapbench/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33872297,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-03T02:00:06.370Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["benchmark","calcium-imaging","forecasting","light-sheet-microcopy","machine-learning","neuroscience","time-series"],"created_at":"2026-06-03T15:31:30.694Z","updated_at":"2026-06-03T15:31:30.756Z","avatar_url":"https://github.com/google-research.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ZAPBench ⚡\n\nThe Zebrafish Activity Prediction Benchmark (ZAPBench) measures progress on the\n problem of predicting cellular-resolution neural activity throughout an entire\n 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).\n\n## Getting started\n\nTo get started with ZAPBench, we provide tutorial-style notebooks in the `colabs/` directory:\n\n- **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)\n- **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)\n- **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)\n- **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)\n\n## Contents\n\nIn addition, this repository contains:\n\n- Code for the forecasting models used in the paper, implemented in `jax`, in the `zapbench/models/` subdirectory.\n- 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.\n- Config for alignment and normalization pipeline of the raw data in `processing/alignment_and_normalization.gin`; see file header for usage.\n- Notebook demonstrating how to load the FFN checkpoint used for segmentation in `processing/ffn_inference.ipynb`.\n- Notebook loading and plotting raw stimulus time-series in `processing/stimuli.ipynb`.\n- A WebGL-viewer for calcium fluorescence data in `fluroglancer/`.\n\n## Datasets\n\n[Further information on associated datasets](http://zapbench-release.storage.googleapis.com/volumes/README.html).\n\n\n## License\n\nApache 2.0\n\n*This is not an officially supported Google product.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fzapbench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle-research%2Fzapbench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fzapbench/lists"}