The Effect Of The Izhikevich Neuron Controlling Parameters On Plastic Neural Network Dynamics

Neuroinformatics is a new research area which combined neurosciences and nonlinear dynamics. From using basic theory and method of nonlinear dynamics, neuroinformatics can make nonlinear analysis to the complicated nervous system, so as to explore the composition and physiological mechanism of nervous system. By reason of the nervous system is a kind of complex network that made up of a large number of neurons by connecting fiber, so study the nonlinear dynamic behavior of neural network is very important to understand our brain’s working mechanism, especially the mechanism of study and memory.In view of recent studies, it is to be perfected that the study of neural network’s dynamics performance and space-time kinetic influenced by the network’s internal factors such as single neuron’s controlling parameters and network’s connection, etc. So we choose the issue to research the influence of inherent element on plastic neural network dynamics. This article main content by following aspect: Chapter 1: We have an introduction of basic theory of neuroinformatics and present some frontier achievements, it gives the significance of our issue.Chapter 2: We choose the Izhikevich neuron model and change time scale of the recovery variable to research its effect on the spike rhythm of plastic network. In our simulation we distinguish the time scale into excite parameter and inhibite parameter by neuron’s excitability. From our study we find that parameter has a critical value and the network’s spike rhythm will include the a u Int Exta a a γ rhythm when is larger than the critical value or is smaller than it. Moreover, when the excite neural scale increase in 0.02~0.03, the rhythm appear in advance, otherwise it will postpone; When the inhibite neural scale decrease in 0.05~0.15, the rhythm appear in advance, otherwise it will postpone.Chapter 3: We solve the Izhikevich neuron’s resting state and make a detailed description of the instability vibration of membrane potential. Here we indicate that the most obvious feature of instability vibration is the fluctuant frequency of membrane potential and recovery variable are higher than synchronous firing state. Furthermore, we study the sensitivity’s effect on spiking behavior of network. There are two conditions separate by input current have or not and we find that parameter has a critical value 0.27 when there is no current. If is larger than 0.27, network’s resting state will not exist and number of the firing neuron in synchronous state will increase with parameter b. When current input exists, network will get a "single neuron spiking" on lower sensitivity. We have a simple discussion on the reason of this behavior. Finally we get a conclusion that lower sensitivity will lead to a long refractory period of neuron and it may not able to motivate other neurons to make them excite. In the statistical result of spiking time interval we get the power-law relation, it indicates that network will show the scale-free properties on the lower sensitivity. v u b b b Finally we also get a expectation of this research area from scientific practice and literature research.