直接从全脑三维视频预测神经活动，避免信息丢失。
Forecasting Whole-Brain Neuronal Activity from Volumetric Video
- 用大感受野模型捕捉全脑远距离依赖关系。
- 在斑马鱼全脑数据集上优于传统追踪方法。
- 适合做高维神经影像建模的研究者参考。
使用荧光钙指示剂的大规模神经活动记录正变得越来越普遍，产生高分辨率的2D或3D视频数据。传统分析流程通过分割感兴趣区域将这些数据压缩为1D时间序列，不可避免地造成信息损失。受其他领域深度学习在原始数据上成功应用的启发，我们探索了直接从体积视频中预测神经活动的潜力。为捕捉高分辨率全脑记录中的长程依赖性，设计了具有大感受野的模型，使其能够整合大脑远端区域的信息。我们研究了预训练的影响，并进行了广泛的模型选择，分析生成准确预测的时空权衡。我们的模型在ZAPBench——一个近期提出的斑马鱼全脑活动预测基准——上优于基于轨迹的预测方法，证明了保留神经活动空间结构的优势。
原文摘要 · Abstract (English)
Large-scale neuronal activity recordings with fluorescent calcium indicators are increasingly common, yielding high-resolution 2D or 3D videos. Traditional analysis pipelines reduce this data to 1D traces by segmenting regions of interest, leading to inevitable information loss. Inspired by the success of deep learning on minimally processed data in other domains, we investigate the potential of forecasting neuronal activity directly from volumetric videos. To capture long-range dependencies in high-resolution volumetric whole-brain recordings, we design a model with large receptive fields, which allow it to integrate information from distant regions within the brain. We explore the effects of pre-training and perform extensive model selection, analyzing spatio-temporal trade-offs for generating accurate forecasts. Our model outperforms trace-based forecasting approaches on ZAPBench, a recently proposed benchmark on whole-brain activity prediction in zebrafish, demonstrating the advantages of preserving the spatial structure of neuronal activity.
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