Research On Random Neural Discharge Induced By Ion Channel Noise

Neural discharge is the carrier of information transmission,which can reveal the basic rules of various neural activities.Mathematical model simulation and neurophysiological experiments are widely used in the related studies.However,neurons are affected by various noises in the biological process during transmitting the information.In the past,Gaussian white noise was added directly as the source of noise,which is unfortunately difficult to find the corresponding source of noise in the real biological system.Because of that,it is important to find more significantly biological channel noise and its influence on the firing activity of neurons,as well as to achieve more simulation of firing patterns and automatic classification of discharge rhythm.In this thesis,the nature of random discharge is obtained from the noise generated by ion transition in ion channel dynamics,a variety of discharge rhythms are unified into the same bifurcation process by using model simulation,and the automatic classification of discharge rhythms is realized by using the model based on sparse auto-encoder.The main results of this paper are as follows:(1)The noise of the ion channel(including white noise and color noise)based on the dynamics of ion channel and the random Chay model based on the noise of fast potassium ion channel were constructed.The noise source was specified,and the influence of specific ion channel noise on neuron discharge was clarified.The periodic alternating rhythm with random characteristics and chaotic rhythm were effectively unified into the same bifurcation process.The sensitivity of the potassium channel was analyzed and discussed.It was found that under the same noise intensity,the potassium channel could change the reliability of pulse sequence,and more likely to cause the random discharge rhythm near the bifurcation point of the adjacent period,which had a greater impact on the neural discharge.(2)The fundamental cause of random discharge was studied based on the generation mechanism of action potential.The deterministic Chay model was improved considering the opening and closing of potassium channel after depolarization.It was a deterministic neuron model without noise.The results showed that the improved model has a powerful simulation capability.It could not only simulate the rich discharge rhythms and bifurcation modes that the original deterministic Chay model could simulate,such as the period adding bifurcation process with chaos,but also simulate the discharge process of period alternating rhythms with random characteristics that only appeared in the random Chay model with noise.In addition,it also effectively unified a variety of discharge rhythms into a bifurcation process,which enriched the diversity of the conversion rules of neural discharge rhythms.Meanwhile,the results also provided a new thought for the research on the unity of determinism-randomicity in the neural discharge field.(3)Realized a method for identifying the randomness of neural discharge rhythm.The interspike intervals were extracted from the pulse sequence under the same long-term dynamic bifurcation background,and then the discharge rhythms were analyzed by combining the nonlinear time series analysis methods and other traditional statistical methods.These methods characterized the stochastic and deterministic characteristics of discharge rhythm and to identify its basic properties.(4)An automatic classification model based on sparse auto-encoder was constructed.Using the discharge data as input,the features extracted by sparse auto-encoder and the nonlinear time series characteristics of discharge rhythm were simply combined and trained in the classifier,then an automatic classification model of discharge rhythm was obtained.The model could provide more accurate classification results quickly,reduced subjectivity and reduced the time of artificial comparison at the same time.It also made the classification of discharge types more intelligent,which helped us to recognize and judge the real patterns of neuron discharge.