We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal-output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically.

中文翻译：

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改变尖峰时间或添加和删除尖峰-神经群体中不同类型的噪声对信号形态的影响。

我们研究了尖峰神经元的群体，这些群体受到独立的噪声过程和强大的公共时间相关输入的影响。我们表明，即使单个神经元的信息传递属性保持不变，输出尖峰对独立噪声的响应也会影响此类群体的信息传递。特别地，我们考虑两个泊松模型，其中独立的噪声（i）添加和删除尖峰（AD模型）或（ii）偏移尖峰时间（STS模型）。我们表明，在两个模型中都可以观察到超阈值随机共振（SSR），其中神经种群传输的信息随着独立噪声的增加而增加。在AD模型中，SSR效应的存在是可靠的，并且与总体大小或噪声频谱统计数据无关。在STS模型中，人口的信息传输特性由噪声的频谱统计确定，从而导致在某些情况下SSR的效果大大增强，而在其他情况下则没有SSR。此外，我们观察到AD模型中不存在的STS模型中信息的高通滤波。我们通过互信息率的下限和频谱相干函数来量化信息传输。为此，我们通过分析得出了两个模型的信号输出互谱，输出功率谱和两个尖峰序列的互谱。我们观察到AD模型中没有STS模型中的信息的高通滤波。我们通过互信息率和频谱相干函数的下限来量化信息传输。为此，我们通过分析得出了两个模型的信号输出互谱，输出功率谱和两个尖峰序列的互谱。我们观察到AD模型中没有STS模型中的信息的高通滤波。我们通过互信息率的下限和频谱相干函数来量化信息传输。为此，我们通过分析得出了两个模型的信号输出互谱，输出功率谱和两个尖峰序列的互谱。