Charactering neural spiking activity evoked by acupuncture through state-space model

In this paper, the underlying action mechanisms of acupuncture during neural spiking activities are studied. First, taking healthy rates as the experimental subjects, different frequencies of acupuncture stimulate their Zusanli points to obtain the evoked electrical signals of spinal dorsal horn neurons. Second, the spikes of the individual wide dynamic range (WDR) neuron are singled out according to wavelet features of different discharge waveforms and transformed into point process spike trains. Then we introduce a state-space model to describe neural spiking activities, in which acupuncture stimuli are the implicit state variables and spike trains induced by acupuncture are the observation variables. Here the implicit state process modulates neural spiking activities when driven by acupuncture. The implicit state and unknown model parameters can be estimated by the expectation-maximization (EM) algorithm. After that, model goodness of fit to spike data is assessed by Kolmogorov-Smirnov (K-S) test. Results show that acupuncture spike trains for different frequencies can be described accurately. Furthermore, the implicit state process involving the information of acupuncture time makes the potential action mechanisms of acupuncture clearer.