Reconstruction And Simulation Of Neuronal Morphology And Firing Pattern For3D Virtual Neuron

As the basic unit of the brain structure, neuron which structure and function con-tains many factors. The geometrical morphology and electrophysiological propertiesare two important aspects. In recent decades, based on neuroscience and adequate ex-perimental data, the researchers use neuron-simulating techniques for enabling effi-cient modeling and simulation of neuronal morphologies, physical and biochemicalchanges. With the extensive research of neuroscience and rapid development of com-puter technology, particularly of technology for visualization, making researchers re-quirement for the simulation results in terms of realistic descriptiveness, accuracy,vividness and intuitive are becoming more and more improved.Base on some of the existent neuron-generating methods, techniques and detailedanalysis of the major ones, this paper presents and illustrates with examples a set ofneuroscience-based neuron-generating algorithm about motoneurons, which can gen-erate realistic simulation results. Finally, we simulate and study the firing pattern ofthe motoneurons. To this end, the paper has done some beneficial and explorative re-searches related to neuronal morphology and firing pattern on the following:The complex and diverse of geometrical morphology is an important characteris-tic of a neuron. While neuronal morphology underlies nervous system connectivityand is a key basis of single cell information processing in the nervous system. Themorphological diversity of dendrites and axons provides an essential substrate forsynaptic integration, signal transmission, network connectivity, and circuit dynamics.In order to realistically describe neuronal morphology, this paper presents a newmethodology which uses genetic regulatory network model with the biological char-acteristics to generate three-dimensional realistic virtual neuron. Moreover, the simu-lation results are reliable as the parameters have been derived from analysis, abstrac-tion and summarization of sufficient experimental data. Finally, we analyze differentparameters in the developmental model impact on the experiment results.In order to make the simulation results more accurate and realistic, as well as cangenerate3D virtual neuron that are anatomically and statistically indistinguishableand conform to experimentally traced real neurons at a very great degree. This paperintroduces the NSGA-II multi-objective evolutionary algorithm, combining genetic regulatory network model with evolutionary computation to generate3D virtual neu-rons together. The core of evolutionary algorithm is segmental duplication and diver-gence model, which plays an important role for evolving genetic regulatory networksand a main driving force for formation the structure of genetic regulatory networks.As can be known by comparison these simulation results are high similarity related toreal neurons to a very great degree.The electrophysiological properties of neuron contain different firing patterns.Applying the simulation results of neuronal morphology to study the physiologicalfunctions of motoneurons, this paper use the simulation software such as NUERONand impact of neuronal morphology, number of synaptic inputs and external stimuluson neuronal firing patterns respectively. The experimental results show that the neu-rons have rich potential firing patterns, different neuronal morphology, number ofsynaptic inputs and external stimulus can generate different potential spike.Due to the growth change of dendrites and axons, making neuronal morphologiesare great diversity and manifold. The geometric morphology and electrical propertieswith combination of other factors are expressing neuronal information transfer func-tion together. In conclusion, with realistic descriptiveness as the fundamental aim, thetheory of neuroscience as the basis and experimental data as the foundation, this paperhas found a sort of realistic neuron-generating method and studies the firing patternsof neurons at the same time.