Modeling And Analysis For Neural Response Evoked By Acupuncture

Acupuncturing at acupoint can evoke effect in corresponding target organ to regulate the body.The nature of acupuncture effect is information regulation,in which neural information regulation plays an important role.Spike information is the produce of neural regulation.In order to understand acupuncture neural response process,this paper builds neuron and neural ensemble response models based on experimental data,which are used to study acupuncture response characteristics and explore the functional mechanism of different acupuncture manipulations.First,acupuncturing can evoke a lot of synchronous spiking,which causes spike wave superposition.The spike sorting based on wave will not work.This paper adopts a model optimization algorithm to identify superimposed waves.Then the model sorting algorithm is used to raw data of neural ensemble evoked by four different acupuncture manipulations and the statistical analysis is applied to sorting results of many experiments to reveal the diversity of response activity.We find that the number of response spike under reinforcing manipulation is far more than reducing manipulation,which mainly embodies in synchronous spiking and is not discovered in wave sorting algorithm.Second,acupuncture is a kind of external mechanical stimulus and neural input signals can’t be accurately detected.The quantitative analysis about input-output system is difficult.This paper models acupuncture as the activated internal state and models acupuncture response process as a probabilistic model based on linear-nonlinear cascade model and Poisson stochastic process.Meanwhich according to state-space model and Bayesian theory,internal state is estimated,which perfectly reconstruct the frequency characteristic of acupuncture.Third,in order to make our probabilistic model more better to describe acupuncture data,Poisson process is replaced by Gamma process,which has two spike characteristics and LIF model is added to our model,which is a single-compartment model.We have balanced the model calculation and neuron morphology.By Bayesian theory,spiking characteristics are estimated,which then are transformed into input parameters of LIF model.According to input parameters,input current and acupuncture response activity are reconstructed.The input current not only reconstructs the frequency characteristic of acupuncture but also the fluctuation changes during acupuncture.Last,under four acupuncture manipulations conditions,we have found diversities of response synchronous spiking.In order to theoretically analyse this phenomenon,this paper builds the probabilistic model of spike activity in neural ensemble.Similarly,the state-space model and Bayesian theory are used to the model decoding algorithm and spike correlations are estimated for different acupuncture manipulations.Results of spike correlations show that reducing manipulation can not evoke the third-order spike correlation compared to reinforcing manipulation.This is the main reason for reduction in synchronous spiking.This paper uses mathematical models to describe acupuncture neural response process and explore acupuncture coding-decoding characteristics.This provides a new method and idea for the quantitative analysis of acupuncture operation principle and functional mechanism.