Koepsell Kilian, Sommer Friedrich T
Redwood Center for Theoretical Neuroscience, Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA 94720, USA.
Biol Cybern. 2008 Nov;99(4-5):403-16. doi: 10.1007/s00422-008-0273-6. Epub 2008 Nov 5.
Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.
通常会忽略与刺激或运动反应无关的周期性神经活动。在此，我们提出了新的工具，用于基于相对于刺激具有准随机相位的周期性神经活动来对信息传递进行建模和量化。我们提出了一个模型来重现振荡尖峰序列的特征，例如尖峰间隔直方图以及尖峰对振荡影响的相位锁定。所提出的模型基于一个非齐次伽马过程，该过程由一个密度函数控制，该密度函数是通常的刺激依赖率与一个准周期函数的乘积。此外，我们提出了一种分析方法，它推广了直接方法（里克等人，《尖峰：探索神经编码》，麻省理工学院出版社，剑桥，1999年；布伦纳等人，《神经计算》12(7):1531 - 1552，2000年）来评估此类数据中的信息内容。我们在猫外侧膝状体中继细胞的记录上展示了这些工具。
