Easton Dexter M
Department of Biological Science and Program in Neuroscience, The Florida State University, Tallahassee, FL 32306-4370, USA.
Physiol Behav. 2005 Oct 15;86(3):407-14. doi: 10.1016/j.physbeh.2005.08.016. Epub 2005 Sep 13.
First-order kinetics is based on simple exponential decay, usually expressed in base e (Naperian) notation. "Nonexponential" processes, for example, S-shaped functions, are frequently modeled as sums of that elemental construct, and the number of rate constants increases with the number of such terms. A powerful descriptive alternative to sums of simple exponentials is the Gompertz function. In Gompertz kinetics, the rate coefficient of an exponential process is assumed to change exponentially with the independent variable. Nonexponential processes are easily modeled, more efficiently and more accurately than is possible with standard kinetics. Application of Gompertz kinetics to neuroscience research topics ranging from cognitive to molecular is presented to illustrate the power of the model: distribution of nerve fiber diameters, conditioning-testing responses of excitable nerve, psychophysical estimates of taste intensity magnitude, time course of synaptic current, and behavior of membrane conductance during voltage clamp of squid axon.
一级动力学基于简单的指数衰减,通常用自然底数e(纳皮尔)表示法来表达。“非指数”过程,例如S形函数,常常被建模为该基本结构的总和,并且速率常数的数量随着此类项的数量增加而增加。一种比简单指数总和更强大的描述性替代方法是冈珀茨函数。在冈珀茨动力学中,指数过程的速率系数被假定随自变量呈指数变化。非指数过程很容易建模,比标准动力学更高效、更准确。本文展示了将冈珀茨动力学应用于从认知到分子等神经科学研究主题,以说明该模型的强大之处:神经纤维直径的分布、可兴奋神经的条件测试反应、味觉强度大小的心理物理学估计、突触电流的时间进程以及鱿鱼轴突电压钳制期间膜电导的行为。