Research On Spike Sorting And Frequency Synchronization Of Neuron Model Response

There are some new methods on computational neuroscience research with the implovement of implanted multi-electrode array. It is the base of neuron or network coding research that how to quickly and accuracily detect neural spikes and classify them to non-homologous neurons. Singular spectrum entropy (SSE) features, reflecting the complexities and informations of spikes under the singular value decomposition (SVD), are extracted and spike-sorting is achieved using c-means clustering method, on account of short-time, nonlinearity and nonstationarity of spikes. According to the differences of non-homologous spikes'waveforms and amplitude fluctuations, the spike- sorting method combining spikes'permutation entropy (PE) features with valley-seeking clustering is used to reflect the complexities and informations of spikes'waveform-variations. It is indicated that the perfect results of clustering are gained by these two new spike-sorting methods. The simulated research on the relation between neuron model response and impulse is an effective approach of neural coding, and it also can provide necessary theoretical basis. So the case of Hodgkin-Huxley (HH) model of multi-input and single-output, which is extensively used in neural characteristics modeling, is researched. The frequency synchronization between model response and impulse on special condition is considered. And the case of double-layer HH neural network model is indicated that the network structure can strengthen the frequency synchronization. The research also shows that frequency coding may be a viable mode of neural coding.The work and research results are as follows in this paper:(1) A new method of non-homologous spikes'features extraction based on SSE is presented in order to reflect the complexities and informations of spikes under SVD. After choosing feature vectors by KS test and other steps, c-means clustering method is used to classify the spikes of non-homologous neurons quickly and accuracily. It is indicated that the features based on SSE of spikes could distinguish the dynamic characteristics of spikes and be an effective basis of spike- sorting.(2) In order to reflect the complexities and informations of spikes'waveform-variations and amplitude fluctuations, a new method of non-homologous spikes'features extraction based on PE is presented. After selecting feature vectors and according to the particularity of feature points'distribution in two-dimensional feature space, spike-sorting is achieved using valley-seeking clustering method. It is indicated that the new spike sorting method using spikes'PE features with valley-seeking clustering can perfectly distinguish the interference of non- homologous spikes.(3) HH model of multi-input and single-output is proposed to accord with the condition of multi-synaptic-input mode, and then frequency synchronization of multi-input model response is researched. The result of two-input case shows that the frequency of spike-train could response the impulse on some condition. Then the frequency synchronization affected by membrane capacitance of model and frequency span of impulse and orher factors is researched.(4) Double-layer HH neural network model is built to simulate synaptic connection mode, and the frequency synchronization affected by network structure is researched. It is indicated by two- input simulated case that the network structure can strengthen the frequency synchronization, and the synchronization is highly significant on condition that two strong-relative variable-frequency- impulses input. The simulated results provide a necessary theoretical basis for the neural electrophysiological experiments.