Neural Clusters Simulation Model Of Self-sustained Firing Activity

What is life? How does the brain work? How do people make choices and decisions? These problems motivate people to understand the brain, the complex system. Scientists are also working through a variety of scientific methods to master its development process and the principles of cognitive function. After six hundred million years’ evolution, the organism have produced a complex neural network with thousand billions neurons connected with each other, to achieve more complex cognitive activities. With the development of science and technology and the pursuit of a better life, principles of brain function and neurological diseases gradually become the focus of scientific research.Functional brain electrical activity has always been the key research point in the area of computational neuroscience. Based on the research in neurobiology experimental results, to simulate some neural network activities in the brain by way of neural network reconstruction. While neuronal cluster self-sustained is a kind of neural network activity exists widely in the brain, which has been proved to be an important manifestation of the brain in working memory and goal oriented behavior etc. According to the results of the study on brain firing activity, we understand that the neural connection of self-sustained firing activity is a kind of functional network connection, and has the characteristics of small world with high coefficient of polymerization. Therefore, we hope to achieve neural clusters self-sustained firing activity through constructing hierarchical neural networks with small world characteristics.Based on the nonlinear Integrate-and-Firing neuron model as network node,constructing a hierarchical neural network simulation model with small-worldcharacteristics, in the entire network the excitatory neurons and inhibitory neurons are selected randomly in the ratio of 4:1. With appropriate model parameters, the new built hierarchical neural network can generate self-sustained firing, besides the overall firing rate is stable within previous 20 s after removal of external stimuli, The average firing rate within one hierarchical small-world neural network is relatively stable, while the firing rate of some hierarchy will appear on the fluctuations.In the analysis of the effects of neural network model parameters on the self-sustained firing activities, the results show that with the number of synaptic connections and short-cut density becomes larger, the overall firing rate of hierarchical neural network in self-sustained firing activity will increase first and then reached saturation. At the same time, with the increase of short-cut density, the greater the neural network’s over-all firing rate will increase more. It is of great importance for revealing the function principle of functional brain nuclei. Further analysis about the influence of overall firing rate concerning the time constant of the membrane potential, conductance of excitatory neurons and conductance of inhibitory neurons, which is consistent with the influence on individual neurons.Finally, we apply the hierarchical neural network to simulate the self-sustained firing activities in rats Y maze experiment associated with the delay period. The delay period and the selection period respectively correspond to two different self-sustained firing activity, and the neurons associated with the delay period will be generated in the first hierarchy of neural network, in addition, the firing rate of action potential of these neurons during the delay period will be much higher. Once turning into the selection period these neurons will turn into another state, during which neurons nearly generate little action potential due to the neural network model with a "memory" characteristic, so as to realize the simulation of self-sustained firing activity associated with the delay period.