Neural decoding on imbalanced calcium imaging data with a network of support vector machines
Neural decoding on imbalanced calcium imaging data with a network of support vector machines
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DOI：
10.1080/01691864.2020.1863259
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发表时间：
2020-12-24
影响因子：
2
通讯作者：
Chen, Rong
中科院分区：
文献类型：
--
作者：
Lee, Kyunghun;Wu, Xiaomin;Chen, Rong
我们提出了一种新颖的钙成像数据神经解码系统。微型钙成像对于检查动物群体神经活动非常有用。我们的神经解码系统是使用精心设计的支持向量机子系统以及基于数据流的系统设计技术开发的，可捕获应用程序的高级结构并实现强大的系统级分析和优化。此外，我们引入了一个处理不平衡数据的框架。这解决了数据集不平衡的问题，该问题通常出现在神经解码应用程序以及生物医学工程和高级机器人技术的各种其他应用程序中。我们开发了一种基于集成学习的方法来解决这个问题。所提出的框架系统地将两个异构模型特征合并到一个组合模型中。通过大量实验，我们使用钙成像数据集评估了所提出的系统，其中记录了背侧纹状体中 D-1 中型多棘神经元的神经活动。结果表明，该系统的 F-1 分数明显优于先前开发的钙成像神经解码系统。
We present a novel neural decoding system for calcium imaging data. Miniature calcium imaging is of great utility for examining population neural activity of animals. Our neural decoding system is developed using a carefully designed support vector machine subsystem together with dataflowbased techniques for system design, which capture the high-level structure of the application and enable powerful system-level analysis and optimization. Also, weintroduce a framework for handling imbalanced data. This addresses a problem of imbalanced datasets, which arises commonly in neural decoding applications, as well as in a wide variety of other applications in biomedical engineering and advanced robotics. We developed an ensemble learning-based method to tackle this problem. The proposed framework systemically incorporates two heterogeneous model characteristics into a combined model. Through extensive experiments, we evaluate the proposed system using calcium imaging datasets in which neural activities of D-1 medium spiny neurons in the dorsal striatum were recorded. The results show that the F-1 score of the proposed system is significantly better than those of previously developed neural decoding systems for calcium imaging.