Institut für Physik, Universitätsstrasse 1, Augsburg, Germany.
Math Biosci. 2013 Sep;245(1):49-55. doi: 10.1016/j.mbs.2013.02.007. Epub 2013 Mar 5.
We numerically investigate the influence of intrinsic channel noise on the dynamical response of delay-coupling in neuronal systems. The stochastic dynamics of the spiking is modeled within a stochastic modification of the standard Hodgkin-Huxley model wherein the delay-coupling accounts for the finite propagation time of an action potential along the neuronal axon. We quantify this delay-coupling of the Pyragas-type in terms of the difference between corresponding presynaptic and postsynaptic membrane potentials. For an elementary neuronal network consisting of two coupled neurons we detect characteristic stochastic synchronization patterns which exhibit multiple phase-flip bifurcations: The phase-flip bifurcations occur in form of alternate transitions from an in-phase spiking activity towards an anti-phase spiking activity. Interestingly, these phase-flips remain robust for strong channel noise and in turn cause a striking stabilization of the spiking frequency.
我们通过数值方法研究了固有信道噪声对神经元系统中延迟耦合动力学响应的影响。在标准 Hodgkin-Huxley 模型的随机修正中，对尖峰的随机动力学进行建模，其中延迟耦合考虑了动作电位沿神经元轴突的有限传播时间。我们根据相应的突触前和突触后膜电位之间的差异，以 Pyragas 型的方式量化这种延迟耦合。对于由两个耦合神经元组成的基本神经元网络，我们检测到特征性的随机同步模式，这些模式表现出多个相位翻转分岔：相位翻转分岔以从同相尖峰活动向反相尖峰活动的交替转换的形式发生。有趣的是，这些相位翻转在强信道噪声下仍然稳健，并且反过来导致尖峰频率的显著稳定。
