Rowat Peter F, Elson Robert C
Institute for Neural Computation, University of California at San Diego, 9500 Gillman Drive, La Jolla, CA 92093-0523, USA.
J Comput Neurosci. 2004 Mar-Apr;16(2):87-112. doi: 10.1023/B:JCNS.0000014104.08299.8b.
We explore the effects of stochastic sodium (Na) channel activation on the variability and dynamics of spiking and bursting in a model neuron. The complete model segregates Hodgin-Huxley-type currents into two compartments, and undergoes applied current-dependent bifurcations between regimes of periodic bursting, chaotic bursting, and tonic spiking. Noise is added to simulate variable, finite sizes of the population of Na channels in the fast spiking compartment. During tonic firing, Na channel noise causes variability in interspike intervals (ISIs). The variance, as well as the sensitivity to noise, depend on the model's biophysical complexity. They are smallest in an isolated spiking compartment; increase significantly upon coupling to a passive compartment; and increase again when the second compartment also includes slow-acting currents. In this full model, sufficient noise can convert tonic firing into bursting. During bursting, the actions of Na channel noise are state-dependent. The higher the noise level, the greater the jitter in spike timing within bursts. The noise makes the burst durations of periodic regimes variable, while decreasing burst length duration and variance in a chaotic regime. Na channel noise blurs the sharp transitions of spike time and burst length seen at the bifurcations of the noise-free model. Close to such a bifurcation, the burst behaviors of previously periodic and chaotic regimes become essentially indistinguishable. We discuss biophysical mechanisms, dynamical interpretations and physiological implications. We suggest that noise associated with finite populations of Na channels could evoke very different effects on the intrinsic variability of spiking and bursting discharges, depending on a biological neuron's complexity and applied current-dependent state. We find that simulated channel noise in the model neuron qualitatively replicates the observed variability in burst length and interburst interval in an isolated biological bursting neuron.
我们探究了随机钠（Na）通道激活对模型神经元中尖峰放电和爆发性放电的变异性及动力学的影响。完整模型将霍奇金-赫胥黎型电流分隔到两个隔室中，并在周期性爆发、混沌爆发和紧张性尖峰放电状态之间经历与施加电流相关的分岔。添加噪声以模拟快速尖峰放电隔室中钠通道群体的可变有限大小。在紧张性放电期间，钠通道噪声会导致峰间间隔（ISI）出现变异性。方差以及对噪声的敏感性取决于模型的生物物理复杂性。它们在孤立的尖峰放电隔室中最小；与一个无源隔室耦合时会显著增加；当第二个隔室也包含慢作用电流时会再次增加。在这个完整模型中，足够的噪声可将紧张性放电转变为爆发性放电。在爆发性放电期间，钠通道噪声的作用取决于状态。噪声水平越高，爆发内尖峰时间的抖动就越大。噪声使周期性状态下的爆发持续时间可变，而在混沌状态下会减小爆发长度持续时间和方差。钠通道噪声模糊了无噪声模型分岔处尖峰时间和爆发长度的尖锐转变。接近这样的分岔时，先前周期性和混沌状态下的爆发行为变得基本无法区分。我们讨论了生物物理机制、动力学解释和生理学意义。我们认为，与有限数量的钠通道相关的噪声可能会对尖峰放电和爆发性放电的内在变异性产生非常不同的影响，这取决于生物神经元的复杂性和与施加电流相关的状态。我们发现，模型神经元中模拟的通道噪声在定性上复制了在孤立的生物爆发性神经元中观察到的爆发长度和爆发间隔的变异性。
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