Firing Dynamics In Excitatory-inhibitory Neuronal Networks

Studying how the nervous system processes information not only promotes understanding of the workings of the brain,but also provides theoretical solu-tions to functional disorders of the brain.The nervous system is consisted of hundreds of millions of neurons.The action potentials of neurons are the most basic units of neural information expression.Therefore,the spatiotemporal dy-namic feature of neuronal networks is the basic manifestation of information processing.For example,synchronization,stochastic resonance and so on are typical phenomena of spatiotemporal dynamics.Synchronization is conducive to the propagation of information in the visual cortex,and detection signals in sensory system is based on stochastic resonance.By the means of modeling and calculation,the study of the nervous system provides theoretical guide and theoretical verification for physiological experiments.In this paper,from the point of view of numerical simulation we try to understand the process of neu-ral processing information by the spatiotemporal dynamics of neural networks,especially the networks with inhibitory neurons(or synapses).Chapter 1 is the introduction,which summarizes the main research content,background and significance of this research.Then it lists the frontier research at home and abroad.On the basis of this,we demonstrate the innovation of this research.Chapter 2 lays the basic knowledge for this study,including the theoretical background of neurobiology,and the basic knowledge of nonlinear dynamics.Then the neuron model and the method of numerical calculation are introduced.For neuron network,we introduce some dynamic measurement to describe col-lective behavior.This chapter is the basis for follow-up work.Chapter 3 studies the synchronization phenomenon of neural network with clustered structure.We discuss the influence of cluster parameters on the syn-chronization.And the dependence of synchronization degree on inhibitory neu-rons is also discussed.The results shows that the clustered structure(more clusters in the network)and inhibitory neurons can both adjust the synchroniza-tion degree of the excitatory neuronal network.When a homogeneous network states at high synchronous state,it will desynchronize by dividing the network into more clusters.So do inhibitory neurons.But inhibitory neurons can pro-mote synchronization degree in turbulence excitatory network.Chapter 4 studies the stochastic resonance phenomenon of neuronal net-works with small-world characteristics.In the excitatory neuron network,the system will generate stochastic multiresonance(SMR)phenomenon.The times of resonance is related to the period of the external subthreshold signal and the noise-induced mean period of the system.The phenomenon of stochastic multiresonance is beneficial to the system's ability to detect subthreshold sig-nals under more than one noise intensity.In addition,inhibitory synapses are introduced into small-world networks.Particularly it is found that inhibition can promote the detection of subthreshold signals.That is,there is a stronger response to the subthreshold signal with small noise intensity.And the detec-tion ability is usually better when the proportion of excitatory and inhibitory synapses is at 4:1.The last chapter is a summary of the whole paper.