de Almeida Licurgo, Idiart Marco, Lisman John E
Neuroscience Program and Physics Institute, Universidade Federal do Rio Grande do Sul, CEP 90040-060 Porto Alegre, Brazil.
J Neurosci. 2009 Jun 10;29(23):7497-503. doi: 10.1523/JNEUROSCI.6044-08.2009.
The role of gamma oscillations in producing synchronized firing of groups of principal cells is well known. Here, we argue that gamma oscillations have a second function: they select which principal cells fire. This selection process occurs through the interaction of excitation with gamma frequency feedback inhibition. We sought to understand the rules that govern this process. One possibility is that a constant fraction of cells fire. Our analysis shows, however, that the fraction is not robust because it depends on the distribution of excitation to different cells. A robust description is termed E%-max: cells fire if they have suprathreshold excitation (E) within E% of the cell that has maximum excitation. The value of E%-max is approximated by the ratio of the delay of feedback inhibition to the membrane time constant. From measured values, we estimate that E%-max is 5-15%. Thus, an E%-max winner-take-all process can discriminate between groups of cells that have only small differences in excitation. To test the utility of this framework, we analyzed the role of oscillations in V1, one of the few systems in which both spiking and intracellular excitation have been directly measured. We show that an E%-max winner-take-all process provides a simple explanation for why the orientation tuning of firing is narrower than that of the excitatory input and why this difference is not affected by increasing excitation. Because gamma oscillations occur in many brain regions, the framework we have developed for understanding the second function of gamma is likely to have wide applicability.
γ振荡在产生主细胞群同步放电方面的作用已广为人知。在此，我们认为γ振荡还有第二个功能：它们选择哪些主细胞放电。这种选择过程通过兴奋与γ频率反馈抑制的相互作用而发生。我们试图了解支配这一过程的规则。一种可能性是固定比例的细胞放电。然而，我们的分析表明，这个比例并不稳定，因为它取决于兴奋在不同细胞间的分布。一种稳健的描述被称为E%-最大值：如果细胞的兴奋度（E）在具有最大兴奋度的细胞的E%范围内且高于阈值，那么这些细胞就会放电。E%-最大值的值可通过反馈抑制延迟与膜时间常数的比值来近似估算。根据测量值，我们估计E%-最大值为5%-15%。因此，一个E%-最大值的胜者全得过程能够区分兴奋度仅有微小差异的细胞群。为了测试这个框架的实用性，我们分析了振荡在V1中的作用，V1是少数几个同时直接测量了放电和细胞内兴奋的系统之一。我们表明，一个E%-最大值的胜者全得过程为放电的方向调谐为何比兴奋性输入的方向调谐更窄以及为何这种差异不受兴奋度增加的影响提供了一个简单的解释。由于γ振荡发生在许多脑区，我们为理解γ的第二个功能而开发的框架可能具有广泛的适用性。
