Department of Chemistry and Biochemistry, and BioCircuits Institute, University of California San Diego, La Jolla, California 92093-0340, USA.
Phys Rev E. 2017 Feb;95(2-1):022414. doi: 10.1103/PhysRevE.95.022414. Epub 2017 Feb 24.
While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.
虽然人们普遍认为信息是通过动作电位或尖峰在神经元中编码的，但对于尖峰中包含的具体编码信息却知之甚少。实验证据表明，第一个尖峰的时间可能是一种节能的编码机制，比后续的尖峰包含更多的神经信息。因此，作为反应潜伏期基础的神经元的生物物理特征引起了相当大的兴趣。在这里，我们研究了通道噪声对从许多其他神经元接收随机输入的 Hodgkin-Huxley 神经元的第一个尖峰潜伏期的影响。由于 Hodgkin-Huxley 神经元的主要特征是通道的随机开启和关闭，因此开放通道数量的波动会导致膜电压的波动，并改变第一个尖峰的时间。我们的结果表明，当神经元具有更多的通道时，（i）第一个尖峰的出现会延迟，（ii）第一个尖峰时间的变化会更大。我们还表明，仅通过了解神经元中的通道数量和突触前神经元的发射速率，就可以通过简单的线性回归准确预测第一个尖峰潜伏期的平均值、中位数和四分位数范围，但仅使用此信息无法预测标准偏差（即神经元抖动）。然后，我们将我们的结果与另一个常用的随机 Hodgkin-Huxley 模型进行比较，并表明更常用的模型夸大了第一个尖峰潜伏期，但可以准确预测第一个尖峰潜伏期的标准偏差。最后，我们根据模拟结果和对两个模型的比较，提出了一个更适合的神经元抖动定义。
