Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
Neural Comput. 2021 Dec 15;34(1):219-254. doi: 10.1162/neco_a_01459.
We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.
我们构建了一个双层、多时间分辨率分类模型，用于解码单个试验的时空模式。该模型以尖峰活动作为输入信号，以二进制行为或认知变量作为输出信号，并使用双层集成分类器表示输入-输出映射。在第一层中，为了解决由于小样本量和输入信号的极高维度而导致的欠定问题，使用 B 样条函数扩展和 L1 正则化逻辑分类器来降低维度并产生稀疏模型估计。通过使用大量具有不同 B 样条节点数的分类器，包括了广泛的神经特征时间分辨率。每个分类器都作为一个基础学习者，将时空模式分类为单个时间分辨率的输出标签的概率。使用引导聚合策略来减少这些分类器的估计方差。在第二层中，另一个 L1 正则化逻辑分类器将第一层分类器的输出作为输入，生成最终的输出预测。该分类器作为一个元学习者，将多个时间分辨率融合在一起，将尖峰时空模式分类为二进制输出标签。我们使用来自大鼠和人类受试者执行记忆依赖行为任务时记录的合成和实验数据来测试此解码模型。结果表明，该方法可以有效地避免过拟合，并在小样本量下实现对输出标签的准确预测。双层、多分辨率分类器通过提取和利用分类中尖峰模式的多分辨率时空特征，始终优于最佳的单层、单分辨率分类器。
