Departments of Statistics and Neurobiology, University of Chicago, Chicago, USA.
Laboratoire de Physique Statistique, CNRS, University Pierre et Marie Curie, Ecole Normale Supérieure, Paris, France.
Curr Opin Neurobiol. 2014 Apr;25:149-55. doi: 10.1016/j.conb.2014.01.005. Epub 2014 Feb 1.
At the single neuron level, information processing involves the transformation of input spike trains into an appropriate output spike train. Building upon the classical view of a neuron as a threshold device, models have been developed in recent years that take into account the diverse electrophysiological make-up of neurons and accurately describe their input-output relations. Here, we review these recent advances and survey the computational roles that they have uncovered for various electrophysiological properties, for dendritic arbor anatomy as well as for short-term synaptic plasticity.
在单个神经元水平上,信息处理涉及将输入的尖峰序列转换为适当的输出尖峰序列。近年来,在经典的神经元作为门控装置的观点基础上,已经开发出了一些模型,这些模型考虑了神经元的多种电生理构成,并准确描述了它们的输入-输出关系。在这里,我们回顾了这些最新进展,并调查了它们为各种电生理特性、树突分支解剖结构以及短期突触可塑性所揭示的计算作用。