Channel Noise from Both Slow Adaptation Currents and Fast Currents Is Required to Explain Spike-Response Variability in a Sensory Neuron
Channel Noise from Both Slow Adaptation Currents and Fast Currents Is Required to Explain Spike-Response Variability in a Sensory Neuron
复制标题
DOI：
10.1523/jneurosci.6231-11.2012
复制
发表时间：
2012-11-28
影响因子：
5.3
通讯作者：
Benda, Jan
中科院分区：
文献类型：
--
作者：
Fisch, Karin;Schwalger, Tilo;Benda, Jan
发放时间的变异性对神经信息处理有很大的影响。然而，对于许多系统，很少有人知道的噪声源引起的尖峰响应的变化。在这里，我们调查蝗虫，一个经典的昆虫模型系统的听觉感受器神经元的尖峰反应的变异性的潜在来源。在低尖峰频率，我们的数据显示负的interspike-interval（ISI）的相关性和ISI分布，匹配逆高斯分布。这些发现可以用与适应电流相互作用的白噪声源来解释。在更高的尖峰频率下，出现更强的峰值分布和正ISI相关性，如从由时间相关（即，色）噪声。随机离子通道的听觉受体神经元的最小电导为基础的模型的模拟排除延迟整流器作为一个可能的噪声源。我们的分析表明，通道噪声的适应电流和受体或钠电流的有色和白色噪声的主要来源，分别。通过比较ISI统计与通用模型，我们发现两个不同的噪声源的强有力的证据。我们的方法不涉及任何可能损害许多感觉系统微妙工作的树突或躯体记录。它可以应用于各种其他类型的神经元，其中通道噪声主导着塑造神经元尖峰统计的波动。
Spike-timing variability has a large effect on neural information processing. However, for many systems little is known about the noise sources causing the spike-response variability. Here we investigate potential sources of spike-response variability in auditory receptor neurons of locusts, a classic insect model system. At low-spike frequencies, our data show negative interspike-interval (ISI) correlations and ISI distributions that match the inverse Gaussian distribution. These findings can be explained by a white-noise source that interacts with an adaptation current. At higher spike frequencies, more strongly peaked distributions and positive ISI correlations appear, as expected from a canonical model of suprathreshold firing driven by temporally correlated (i.e., colored) noise. Simulations of a minimal conductance-based model of the auditory receptor neuron with stochastic ion channels exclude the delayed rectifier as a possible noise source. Our analysis suggests channel noise from an adaptation current and the receptor or sodium current as main sources for the colored and white noise, respectively. By comparing the ISI statistics with generic models, we find strong evidence for two distinct noise sources. Our approach does not involve any dendritic or somatic recordings that may harm the delicate workings of many sensory systems. It could be applied to various other types of neurons, in which channel noise dominates the fluctuations that shape the neuron's spike statistics.