Based On Hr Neuron Discharge Model And Neural Network Synchronization Problems Of Research

The study of the communication and coordinated operation of human neuralsystems is one of the greatest challenges of modern neural science, and the foundationof the study is modeling and simulation of human brain neurons. Since the early days ofthe last century, different types of neuron models were proposed by biologists andphysicists. These models vary a lot and have their own advantages and disadvantages.Different types of mathematical models of neurons exist, such as Hogukin-Huxley,Hindmarsh-Rose, etc. The reason why HR-type model is chosen is1) it has relativelysimple equation patterns and capable of producing rich firing patterns exhibited in realneurons and2) it is the kind of model that based on the electrical activity of neurons; theHH model only describes the features of neurons within biology and chemistry, which isimpossible to be simulated on the computer and then be implemented by circuits.In this paper, the neurons are modeled and simulated within Matlab based on theHR model. The effect of two mean control parameters, r and the input current I, to theneuron firing is discussed. Then the neuron coding sequence is studied and the effect ofparameters r and I to the firing rate and coding result is simulated, then study the codeof the model in the way of Non-sequence histogram, Scatter diagram, and Return mapdiagram. This work studies the neuron with tentative exploration, conjectures the basicproperties of neurons, lays the foundation of further research and offers the basis for thedesigning of electronic products on HR neuron model.In addition, there is an explosive chaotic phenomenon in the HR neuron model.Dhamala and other scholars have found that in a time delay two HR neuron can besynchronized. In this paper, we introduce three kinds of neuron network, fullyconnected network, star connection network, closest domain network, and respectivelystudy each of them on the effect of the parameter time delay τ and coupling strength αon the synchronization of neurons. Beside, we add noise to fully connected neuronnetwork, changing the noise intensity to see the effect of HR neuronal synchronization.