ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929740635439104 |
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| author | Lueckmann, Jan-Matthis Immer, Alexander Chen, Alex Bo-Yuan Li, Peter H. Petkova, Mariela D. Iyer, Nirmala A. Hesselink, Luuk Willem Dev, Aparna Ihrke, Gudrun Park, Woohyun Petruncio, Alyson Weigel, Aubrey Korff, Wyatt Engert, Florian Lichtman, Jeff W. Ahrens, Misha B. Januszewski, Michał Jain, Viren |
| author_facet | Lueckmann, Jan-Matthis Immer, Alexander Chen, Alex Bo-Yuan Li, Peter H. Petkova, Mariela D. Iyer, Nirmala A. Hesselink, Luuk Willem Dev, Aparna Ihrke, Gudrun Park, Woohyun Petruncio, Alyson Weigel, Aubrey Korff, Wyatt Engert, Florian Lichtman, Jeff W. Ahrens, Misha B. Januszewski, Michał Jain, Viren |
| contents | Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of over 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into forecasting methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish Lueckmann, Jan-Matthis Immer, Alexander Chen, Alex Bo-Yuan Li, Peter H. Petkova, Mariela D. Iyer, Nirmala A. Hesselink, Luuk Willem Dev, Aparna Ihrke, Gudrun Park, Woohyun Petruncio, Alyson Weigel, Aubrey Korff, Wyatt Engert, Florian Lichtman, Jeff W. Ahrens, Misha B. Januszewski, Michał Jain, Viren Neurons and Cognition Computer Vision and Pattern Recognition Machine Learning Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of over 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into forecasting methods. |
| title | ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.02618 |