Royal Institute of Technology, Department of Computational Biology, Roslagstullsbacken 35, 11421 Stockholm, Sweden.
Brain Res. 2012 Jan 24;1434:152-61. doi: 10.1016/j.brainres.2011.07.055. Epub 2011 Aug 2.
Simulation work has recently shown that attractor networks can reproduce Poisson-like variability of single cell spiking, with coefficient of variation (Cv(2)) around unity, consistent with cortical data. However, the use of local variability (Lv) measures has revealed area- and layer-specific deviations from Poisson-like firing. In order to test these findings in silico we used a biophysically detailed attractor network model. We show that Lv well above 1, specifically found in superficial cortical layers and prefrontal areas, can indeed be reproduced in such networks and is consistent with periodic replay rather than persistent firing. The memory replay at the theta time scale provides a framework for a multi-item memory storage in the model. This article is part of a Special Issue entitled Neural Coding.
最近的模拟工作表明，吸引子网络可以再现单细胞尖峰的泊松样变异性，变异系数（Cv(2)）约为 1，与皮质数据一致。然而，局部变异性（Lv）测量的使用揭示了与泊松样放电的区域和层特异性偏差。为了在计算机中测试这些发现，我们使用了一个具有生理细节的吸引子网络模型。我们表明，Lv 远高于 1，特别是在前脑皮层和浅层皮层中发现的，在这样的网络中确实可以再现，并且与周期性重放而不是持续放电一致。在 theta 时间尺度上的记忆重放为模型中的多项目记忆存储提供了一个框架。本文是主题为“神经编码”的特刊的一部分。
