Spike time reliability (STR) refers to the phenomenon in which repetitive applications of a frozen copy of one stochastic signal to a neuron trigger spikes with reliable timing while a constant signal fails to do so. Observed and explored in numerous experimental and theoretical studies, STR is a complex dynamic phenomenon depending on the nature of external inputs as well as intrinsic properties of a neuron. The neuron under consideration could be either quiescent or spontaneously spiking in the absence of the external stimulus. Focusing on the situation in which the unstimulated neuron is quiescent but close to a switching point to oscillations, we numerically analyze STR treating each spike occurrence as a time localized event in a model neuron. We study both the averaged properties as well as individual features of spike-evoking epochs (SEEs). The effects of interactions between spikes is minimized by selecting signals that generate spikes with relatively long interspike intervals (ISIs). Under these conditions, the frequency content of the input signal has little impact on STR. We study two distinct cases, Type I in which the f-I relation (f for frequency, I for applied current) is continuous and Type II where the f-I relation exhibits a jump. STR in the two types shows a number of similar features and differ in some others. SEEs that are capable of triggering spikes show great variety in amplitude and time profile. On average, reliable spike timing is associated with an accelerated increase in the "action" of the signal as a threshold for spike generation is approached. Here, "action" is defined as the average amount of current delivered during a fixed time interval. When individual SEEs are studied, however, their time profiles are found important for triggering more precisely timed spikes. The SEEs that have a more favorable time profile are capable of triggering spikes with higher precision even at lower action levels.

中文翻译：

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两种阈值动态中尖峰时间可靠性的计算研究。

尖峰时间可靠性 (STR) 是指一种现象，其中将一个随机信号的冻结副本重复应用到神经元触发具有可靠定时的尖峰，而恒定信号却没有这样做。在众多实验和理论研究中观察和探索，STR 是一种复杂的动态现象，取决于外部输入的性质以及神经元的内在特性。在没有外部刺激的情况下，所考虑的神经元可以是静止的，也可以是自发的。关注未受刺激的神经元处于静止状态但接近振荡的切换点的情况，我们对 STR 进行数值分析，将每个尖峰发生视为模型神经元中的时间局部事件。我们研究了尖峰诱发时期 (SEE) 的平均特性和个体特征。通过选择产生具有相对较长的尖峰间隔 (ISI) 的尖峰的信号，可以最大限度地减少尖峰之间相互作用的影响。在这些条件下，输入信号的频率成分对 STR 的影响很小。我们研究了两种不同的情况，其中 fI 关系（f 代表频率，I 代表施加电流）是连续的类型 I 和其中 fI 关系呈现跳跃的类型 II。这两种类型的 STR 显示出许多相似的特征，但在其他一些方面有所不同。能够触发尖峰的 SEE 在幅度和时间分布方面表现出极大的多样性。平均而言，随着接近尖峰生成的阈值，可靠的尖峰定时与信号“作用”的加速增加相关。在这里，“行动” 定义为在固定时间间隔内输送的平均电流量。然而，当研究单个 SEE 时，发现它们的时间曲线对于触发更精确的定时尖峰很重要。具有更有利时间曲线的 SEE 能够以更高的精度触发尖峰，即使在较低的动作级别。