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首页> 外文期刊>Journal of Computational Neuroscience >Variability of bursting patterns in a neuron model in the presence of noise
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Variability of bursting patterns in a neuron model in the presence of noise

机译：存在噪声时神经元模型中爆发模式的变异性

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摘要

Spiking and bursting patterns of neurons are characterized by a high degree of variability. A single neuron can demonstrate endogenously various bursting patterns, changing in response to external disturbances due to synapses, or to intrinsic factors such as channel noise. We argue that in a model of the leech heart interneuron existing variations of bursting patterns are significantly enhanced by a small noise. In the absence of noise this model shows periodic bursting with fixed numbers of interspikes for most parameter values. As the parameter of activation kinetics of a slow potassium current is shifted to more hyperpolarized valuesrnof the membrane potential, the model undergoes a sequence of incremental spike adding transitions accumulating towards a periodic tonic spiking activity. Within a narrow parameter window around every spike adding transition, spike alteration of bursting is deter-ministically chaotic due to homoclinic bifurcations of a saddle periodic orbit. We have found that near these transitions the interneuron model becomes extremely sensitive to small random perturbations that cause a wide expansion and overlapping of the chaotic windows. The chaotic behavior is characterized by positive values of the largest Lyapunov exponent, and of the Shannon entropy of probability distribution of spike numbers per burst. The windows of chaotic dynamics resemble the Arnold tongues being plotted in the parameter plane, where the noise intensity serves as a second control parameter. We determine the critical noise intensities above which the interneuron model generates only irregular bursting within the overlapped windows.
机译：神经元的尖峰和爆发模式具有高度的可变性。单个神经元可以表现出内源性的各种突发模式，响应于突触引起的外部干扰或内在因素（例如通道噪声）而发生变化。我们认为，在水heart心脏中间神经元的模型中，爆破模式的现有变化会因小噪声而明显增强。在没有噪声的情况下，该模型显示了针对大多数参数值的固定尖峰间隔的周期性突发。当缓慢的钾电流的激活动力学参数移至膜电位以外的更多超极化值时，该模型会经历一系列递增的尖峰添加，累积的过渡向着周期性的强加标活动。在每个尖峰添加过渡周围的狭窄参数窗口内，由于鞍形周期性轨道的同斜分叉，突发的尖峰改变在确定性上是混乱的。我们发现，在这些转变附近，中间神经元模型对小的随机扰动变得极为敏感，这些随机扰动引起混沌窗口的广泛扩展和重叠。混沌行为的特征在于最大Lyapunov指数的正值，以及每个突发的尖峰数的概率分布的Shannon熵。混沌动力学的窗口类似于在参数平面上绘制的Arnold舌头，其中噪声强度用作第二个控制参数。我们确定临界噪声强度，在该强度以上，中间神经元模型在重叠窗口内仅生成不规则爆裂。

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