Chaotic Synchronization And Application Of ML Neuron System Based On Memristor

Neuron is the most basic structure and function unit of nervous system.Constructing neuron model according to the real physiological environment of neuron is helpful to study the dynamic behavior of neuron in real situation.The three-dimensional Morris-lecar(ML)neuron model was an improved model based on the two-dimensional ML neuron model.Although the model took into account the slow-varying regulating current,it did not consider the influence of the induced electromagnetic field generated when ions inside and outside the neuron membrane move across the membrane to the neuron discharge rhythm.Therefore,combined with the characteristics of the memristor,this thesis constructs a four-dimensional ML neuron model by adding a magneto-controlled memristor into the three-dimensiona ML neuron model,which can better simulate the real situation of biological neurons and contribute to further research on the dynamic behavior of neurons.Meanwhile,because of the natural confidentiality of chaotic signals,it is especially suitable for the application of secure communication.Therefore,based on the study of neuron chaotic synchronization,this thesis studies the application of neuron chaotic synchronization system in secure communication.Firstly,the dynamic behavior of ML neuron model based on memristor is compared with that of three-dimensional ML neuron model.It is found that the neuron model constructed in this thesis is not only complex in structure,but also has richer discharge mode and wider chaos interval,which makes it easier for neurons to enter chaos state.At the same time,the effects of other parameters in the model on neuron discharge rhythm are studied by using time history diagram,phase diagram and interspike interval bifurcation diagram.It is proved that the change of parameters in the model can cause neuron to produce a variety of discharge states,and the parameters in the model have a great influence on neuron discharge states.Secondly,chaos synchronization of ML neurons based on memristor is studied.Chaos characteristics of neuron model is studied by using maximum lyapunov exponent and dissipative theory.Then,the direct coupling synchronization and flux coupling synchronization of neuron model are studied by combining theoretical analysis and numerical simulation.Through theoretical derivation and simulation experiments,it is proved that both methods can achieve chaotic synchronization of neurons,and the magnetic flux coupling method based on memristor is closer to the real situation of biological neuron synaptic connection.At the same time,the influence of noise on the synchronization behavior of neurons is studied.Through simulation experiments,it is found that appropriate intensity of noise can induce neurons to achieve synchronization faster.Finally,the ML neuron chaotic synchronization system based on memristor is applied to secure communication.According to lyapunov stability theory,the synchronization controller is designed to realize the chaotic synchronization of two neuron systems.The system is used to encrypt and decrypt the information signal.The experimental results show that the system can encrypt and decrypt the information signal well.Then,the system is applied to the speech signal encryption and decryption.The simulation results and correlation analysis show that the system has a good encryption and decryption effect on the speech signal,and high security and confidentiality.