Analysis of Neuronal Spike Trains, Deconstructed.
Analysis of Neuronal Spike Trains, Deconstructed.
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DOI：
10.1016/j.neuron.2016.05.039
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发表时间：
2016-07-20
期刊：
影响因子：
16.2
通讯作者：
Kleinfeld D
中科院分区：
文献类型：
--
作者：
Aljadeff J;Lansdell BJ;Fairhall AL;Kleinfeld D
当信息流经大脑时，神经元的放电从编码动物所感知的世界发展到驱动后续行为的运动输出。定量神经科学的一个更容易处理的目标是开发预测模型，将感觉或运动流与神经元放电联系起来。在这里，我们回顾和对比用于完成这一任务的分析工具。我们专注于类的模型，其中外部变量与一个或多个特征向量进行比较，以提取低维表示，尖峰和其他变量的历史可能会被纳入，这些因素进行非线性变换，以预测尖峰的发生。我们说明这些技术在应用程序中的数据集的不同程度的复杂性。特别是，我们解决的拟合模型中存在的强相关性的外部变量，发生在自然的感官刺激和运动。预测和测量的尖峰序列之间的光谱相关性被引入到对比不同方法的相对成功。
As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.