论文标题
表征具有非平稳外部输入的子采样系统的传播动力学
Characterizing spreading dynamics of subsampled systems with non-stationary external input
论文作者
论文摘要
许多具有传播动态的系统，例如神经网络中的尖峰传播和传染病的传播，可以通过自回归模型近似。模型参数的估计可能会通过仅观察到系统的一部分（子采样）和潜在的时间依赖性参数的实验限制而变得复杂，从而导致错误的估计值。我们在估计具有某些非平稳外部输入的系统的传播速率时，分析表明了如何克服子采样偏差。这种方法很容易适用于基于试验的实验设置和季节性波动，如猴子前额叶皮层的尖峰记录以及诺如病毒和麻疹的扩散所示。
Many systems with propagation dynamics, such as spike propagation in neural networks and spreading of infectious diseases, can be approximated by autoregressive models. The estimation of model parameters can be complicated by the experimental limitation that one observes only a fraction of the system (subsampling) and potentially time-dependent parameters, leading to incorrect estimates. We show analytically how to overcome the subsampling bias when estimating the propagation rate for systems with certain non-stationary external input. This approach is readily applicable to trial-based experimental setups and seasonal fluctuations, as demonstrated on spike recordings from monkey prefrontal cortex and spreading of norovirus and measles.
