Department of Automatic Control and Systems Engineering, University of Sheffield, U.K.
Neural Comput. 2011 Aug;23(8):1967-99. doi: 10.1162/NECO_a_00156. Epub 2011 Apr 26.
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.
我们提出了一种变分贝叶斯（VB）方法，用于具有点过程观测的状态空间模型的状态和参数推断，这是一种用于处理尖峰数据信号处理的生理上合理的模型。我们还给出了变分平滑器的推导，以及一种有效的在线滤波算法，该算法也可用于跟踪生理参数的变化。该方法在模拟数据上进行了评估，并与期望最大化以及蒙特卡罗估计技术进行了比较，以评估所提出方法的准确性。VB 滤波器还在味觉反应神经细胞数据集上进行了评估，结果表明，该方法可以有效地实时捕获神经反应的动态变化。
