Lundstrom Brian Nils, Famulare Michael, Sorensen Larry B, Spain William J, Fairhall Adrienne L
Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA.
J Comput Neurosci. 2009 Oct;27(2):277-90. doi: 10.1007/s10827-009-0142-x. Epub 2009 Apr 8.
Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the f-I curve. We introduce a novel classification of neurons into Types A, B-, and B+ according to how f-I curves are modulated by input fluctuations. In Type A neurons, the f-I curves display little sensitivity to input fluctuations when the mean current is large. In contrast, Type B neurons display sensitivity to fluctuations throughout the entire range of input means. Type B- neurons do not fire repetitively for any constant input, whereas Type B+ neurons do. We show that Type B+ behavior results from a separation of time scales between a slow and fast variable. A voltage-dependent time constant for the recovery variable can facilitate sensitivity to input fluctuations. Type B+ firing rates can be approximated using a simple "energy barrier" model.
神经元反应通常以放电率作为刺激均值的函数来表征，即f-I曲线。我们根据f-I曲线如何受输入波动调制，将神经元分为A、B-和B+三种新型类别。在A类神经元中，当平均电流较大时，f-I曲线对输入波动的敏感性较低。相比之下，B类神经元在整个输入均值范围内都对波动敏感。B-类神经元对于任何恒定输入都不会重复放电，而B+类神经元则会。我们表明，B+类行为源于慢速和快速变量之间的时间尺度分离。恢复变量的电压依赖性时间常数可促进对输入波动的敏感性。B+类放电率可以使用一个简单的“能量屏障”模型来近似。
