Encoding And Decoding Of Acupuncture Electrical Signals

Neural system characterizes information of external stimulation by spa-tiotemporal encoding. However, there are rarely reports about the encodingform of acupuncture stimulation and the model of electrical signal transmissionpath. We try to explore the underlying mechanism of acupuncture by construct-ing and analyzing the feedforward neuronal model and mining the experimentalsignals based on encoding, decoding algorithm and complex network mappingmethod.First, we constructed a feedforward network (FFN) of FitzHugh-Nagumo(FHN)neurons according to the feedforward pathway of acupuncture signal. Here, theacupuncture stimulation is equivalent to noise. It is found that acupuncture playsan important role in modulating the transmission of fring rate and spiking reg-ularity. Furthermore, noise could induce coherent resonance in the feedforwardnetwork. Therefore, acupuncture with certain intensity can induced resonance inneuronal network, which would afect the activities of the neural system. More-over, after assuming the acupuncture stimuli to be aperiodic signal, it is foundthat resonance can also be induced by heterogenous aperiodic signals, and pa-rameters of the aperiodic signal can modify the resonance of the coupled network.These studies imply that resonance and synchronization may be the mechanismof acupuncture.To further understand the mechanism of acupuncture, experiments are de-signed that manual acupuncture (MA) manipulations with diferent types andfrequencies are taken at ’Zusanli’ points of experiment rats. We found the spa-tiotemporal coding pattern of diferent acupuncture manipulations. The difer-ences between features of manipulation ’nb’’nx’ and those of manipulation ’tb’’tx’ are obvious. However, neuronal selectivity of encoding is not obvious whenthe same manipulation is taken with diferent frequencies. Neuronal adaptivityand saturation phenomenon are also observed when acupuncture with diferentfrequencies are taken. Types of acupuncture manipulations taken on the rats are inferred witha high probability (about90%) by Bayesian decoding algorithm based on theresponse of multiple neurons. These results are proved to be signifcant by s-tatistical analysis. Furthermore, mutual information is applied to quantify thedecoding process. These studies may help to construct the interface betweenneural systems and machines and improve the clinical study. We also proposecomplex network mapping method to analyze the signal. It indicates that theinherent characteristics of MA ’nb’’nx’ and those of MA ’tb’’tx’ are diferent.After compare the experimental results and the model analysis, we foundthat some key properties in experiment can be reproduced by the feedforwardmodel. This fnding imply that the modelling strategy is reasonable. The combi-nation of experimental and computational study is helpful to improve the clinicaltreatment of acupuncture.