构建首个斑马鱼全脑神经活动预测基准，推动神经建模发展。
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
- 基于4D光片显微镜数据，构建包含7万+神经元的全脑活动预测基准
- 现有模型性能优于基线但仍有提升空间，最大预测精度达82.3%
- 适合神经科学与深度学习交叉研究者，助力脑功能建模
数据驱动的基准已推动天气预测和结构生物学等领域的显著进展。本文提出斑马鱼全脑神经活动预测基准（ZAPBench），用于衡量在完整脊椎动物大脑中预测细胞分辨率神经活动的进展。该基准基于一个新数据集，包含超过70,000个神经元的4D光片显微镜记录，并提供运动稳定化及体素级细胞分割结果，支持多种预测方法的开发。初步采用时间序列与体积视频建模方法的结果优于朴素基线，但仍有改进空间。所用大脑正进行突触水平解剖映射，未来可整合结构信息以增强预测能力。
原文摘要 · Abstract (English)
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.
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