Aljadeff Johnatan, Lansdell Benjamin J, Fairhall Adrienne L, Kleinfeld David
Department of Physics, University of California, San Diego, San Diego, CA 92093, USA; Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA.
Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA.
Neuron. 2016 Jul 20;91(2):221-59. doi: 10.1016/j.neuron.2016.05.039.
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.
当信息在大脑中流动时，神经元放电从对动物所感知的世界进行编码，发展到驱动后续行为的运动输出。定量神经科学中一个更容易处理的目标是开发将感觉或运动流与神经元放电联系起来的预测模型。在这里，我们回顾并对比用于完成这项任务的分析工具。我们关注的模型类别是，将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，潜在地纳入尖峰历史和其他变量，并对这些因素进行非线性变换以预测尖峰的出现。我们在应用于不同复杂程度的数据集时展示这些技术。特别是，我们处理在外部变量存在强相关性的情况下模型的拟合问题，这在自然感觉刺激和运动中都会出现。引入预测尖峰序列与实测尖峰序列之间的频谱相关性，以对比不同方法的相对成功程度。
