Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron
Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron
复制标题
DOI：
10.1016/j.jtbi.2004.08.030
复制
发表时间：
2005-02-21
影响因子：
2
通讯作者：
Longtin, A
中科院分区：
文献类型：
--
作者：
Lindner, B;Longtin, A
我们研究了白噪声驱动的具有时间依赖性阈值的整合和激发（IF）神经元。假设阈值的修改很小，我们给出了峰间期的均值和方差的解析表达式。结果表明，与阈值不变的情况相比，时间间隔的变异性可以变得更小或更大，这取决于阈值的衰减率。我们还表明，相对可变性是最小的阈值的某个有限的衰减率。此外，对于缓慢的阈值衰减，泄漏IF模型示出了最小的变异系数，只要神经元的发射率匹配阈值的衰减率。如果通过改变噪声强度或平均输入电流来改变点火速率，则可以看到这种新颖的效果。(C)2004爱思唯尔有限公司保留所有权利。
We study a white-noise driven integrate-and-fire (IF) neuron with a time-dependent threshold. We give analytical expressions for mean and variance of the interspike interval assuming that the modification of the threshold value is small. It is shown that the variability of the interval can become both smaller or larger than in the case of constant threshold depending on the decay rate of threshold. We also show that the relative variability is minimal for a certain finite decay rate of the threshold. Furthermore, for slow threshold decay the leaky IF model shows a minimum in the coefficient of variation whenever the firing rate of the neuron matches the decay rate of the threshold. This novel effect can be seen if the firing rate is changed by varying the noise intensity or the mean input current. (C) 2004 Elsevier Ltd. All rights reserved.