Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution still images of a marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 and 2015 using the NEPTUNE seafloor cabled observatory, off the west coast of Vancouver Island, Canada. We applied semantic segmentation with the Unet architecture with some modifications, including adapting parts of the architecture to be more applicable to three-channel images (RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-fold cross-validation. This study is a first step towards analyzing trends in the behavior of a demosponge in an environment that experiences severe seasonal and inter-annual changes in climate. The end objective is to correlate changes in sponge size (activity) over seasons and years with environmental variables collected from the same observatory platform. Our work provides a roadmap for others who seek to cross the interdisciplinary boundaries between biology and computer science.

中文翻译：

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机器学习应用卷积神经网络和 Unet 架构来预测和分类 Demosponge 行为

生物数据集正变得越来越信息密集，这使得使用基于计算机科学的分析变得有效。我们使用卷积神经网络 (CNN) 和特定的 CNN 架构 Unet 来研究海绵随时间的行为。我们分析了海洋海绵Suberites concinnus的每小时高分辨率静态图像的大量时间序列(Demospongiae, Suberitidae) 于 2012 年至 2015 年间使用位于加拿大温哥华岛西海岸的 NEPTUNE 海底电缆天文台捕获。我们在 Unet 架构中应用了语义分割，并进行了一些修改，包括调整架构的某些部分以更适用于三通道图像 (RGB)。使该模型成功的一些更改是在每个卷积层之后使用了骰子损失系数、Adam 优化器和 dropout 函数，它们分别提供了高达 0.03、0.98 和 0.97 的损失、准确度和骰子分数。该模型通过五重交叉验证进行测试。这项研究是在经历严重的季节性和年际气候变化的环境中分析海绵纲动物行为趋势的第一步。最终目标是将海绵大小（活动）随季节和年份的变化与从同一天文台平台收集的环境变量相关联。我们的工作为寻求跨越生物学和计算机科学之间跨学科界限的其他人提供了路线图。