Kempter R, Gerstner W, van Hemmen J L, Wagner H
Theoretische Physik, Physik Department, München, James Franck Strasse, D 85747, DE.
Neural Comput. 1998 Nov 15;10(8):1987-2017. doi: 10.1162/089976698300016945.
How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code.
如果输入从随机活动变为振荡相干但有噪声的活动，神经元如何改变其平均输出放电率？神经元动力学和输入统计的关键参数是什么？为了回答这些问题，我们研究了积分发放神经元的重合检测特性。我们推导了一个表达式，表明重合检测如何依赖于神经元参数。具体而言，我们展示了重合检测如何依赖于突触后响应函数的形状、突触数量和输入统计，并证明存在一个最佳阈值。我们的研究结果可用于根据神经元参数预测神经元是否以及在多大程度上可以作为重合探测器，从而将时间编码转换为速率编码。
