Ortiz-Rosario Alexis, Adeli Hojjat, Buford John A
Departments of Biomedical Engineering, The Ohio State University, United States.
Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States.
Behav Brain Res. 2017 Jan 15;317:226-236. doi: 10.1016/j.bbr.2016.09.022. Epub 2016 Sep 17.
Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.
研究人员通常依靠简单的方法来确定神经元在特定运动任务中的参与情况。传统方法是检查大量神经元，并根据研究者的专业知识主观地将神经元分为不同组。在神经元群体数量较少的情况下，仔细检查这些神经元记录及其放电率以避免数据遗漏是合理的。本文提出了一种新方法，用于将与行为任务相关记录的神经元自动客观地分类成组。通过识别特定组中神经元的特征，研究者随后可以根据它们与任务的关系识别神经元的功能类别。该方法基于多信号分类（MUSIC）算法的集成，以从放电率中提取相关特征，并基于期望最大化高斯混合算法（EM-GMM）对提取的特征进行聚类。该方法能够基于特定信号特征自动识别和聚类相似的放电率分布。使用经验小波变换（EWT）来验证在MUSIC伪谱中发现的特征以及该方法捕获的所得信号特征。此外，该方法用于检查神经元的行为元素，以在生理上验证该模型。使用从清醒行为的非人类灵长类动物收集的一组数据对该方法进行了测试。
