A general method to generate artificial spike train populations matching recorded neurons

We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering our amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified for recorded spike trains from cerebellum and cerebral cortex. We found that ASTs generated with this method can closely follow the firing rate and local as well as global spike time variance and power spectrum. The method is primarily intended to generate well-controlled spike train populations as inputs for dynamic clamp studies or biophysically realistic multicompartmental models. Such inputs are essential to study detailed properties of synaptic integration with well-controlled input patterns that mimic the in vivo situation while allowing manipulation of input rate covariances at different time scales.

我们开发了一种通用方法，用于生成与记录神经元统计特性相匹配的人工锋电位序列（artificial spike trains，ASTs）。
该方法基于对记录得到的锋电位序列计算高斯局部放电率函数，由此得到放电率模板；基于此类模板，可通过带不应期的伽马分布过程采样生成ASTs。
可从同一套放电率模板中采样得到多组锋电位序列实例。
值得注意的是，我们可通过对放电率模板执行简单的算法变换来调控锋电位序列间的放电率协方差，例如对特定频段进行滤波或放大，以及添加与行为相关的放电率调制信号。
本方法首先通过正弦波放电率模板等替代数据验证了其准确性与局限性，随后利用来自小脑与大脑皮层的实测锋电位序列完成了验证。
我们发现，采用本方法生成的ASTs能够精准匹配实测序列的放电率、局部与全局锋电位时间方差以及功率谱特性。
本方法主要用于生成可控性良好的锋电位序列群体，作为动态钳制（dynamic clamp）研究或具有生物物理真实性的多室神经元模型的输入数据。
这类输入数据对于研究突触整合的精细特性至关重要：通过模拟活体（in vivo）环境下的可控输入模式，同时实现在不同时间尺度上对输入放电率协方差的精准调控。