论文标题
部分可观测时空混沌系统的无模型预测
Deep Learning Architectures for FSCV, a Comparison
论文作者
论文摘要
我们检查了多个深神经网络（DNN）架构，以适合从碳纤维电极上收集的体外快速扫描环状伏安法（FSCV）数据中预测神经递质浓度。适用性取决于“探针外”情况，人为诱导的电噪声的响应以及预测模型何时会出现对给定探针错误的能力确定的。这项工作通过关注此特定任务来扩展时间序列分类模型的先前比较。它通过使用更大的数据集并通过将最新的进步纳入深神经网络中，将机器学习的先前应用程序扩展到FSCV任务。 Inceptiontime Architecture是一个深度卷积神经网络，具有测试模型的最佳绝对预测性能，但更容易受到噪声的影响。幼稚的多层感知构建结构的预测错误是第二个最低的预测误差，并且受人工噪声的影响较小，这表明卷积对于这项任务可能并不像人们怀疑的那样重要。
We examined multiple deep neural network (DNN) architectures for suitability in predicting neurotransmitter concentrations from labeled in vitro fast scan cyclic voltammetry (FSCV) data collected on carbon fiber electrodes. Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. This work extends prior comparisons of time series classification models by focusing on this specific task. It extends previous applications of machine learning to FSCV task by using a much larger data set and by incorporating recent advancements in deep neural networks. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect.
