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神经元集群的自持续放电活动是大脑内广泛存在的现象,其被证实在大脑的工作记忆与目标导向等行为中有重要体现。作者以非线性的整合发放(integrate-and-firing,IF)神经元模型为网络节点,构建了具有小世界特征的层次网络仿真模型,以研究自持续活动中神经元发放的一些特性。在合适的模型参数下,层次网络能产生自持续放电活动,其整体发放频率在撤掉外部刺激之后的20 s内比较稳定,而层次内部发放频率的高低与层次顺序无关。整体发放频率关于突触连接数量与短路径密度都呈现出先正关系增长再达到饱和的趋势,同时,规模越大的神经元网络的整体发放频率对短路径密度更为敏感。研究结果对揭示大脑神经元功能性核团之间的相互作用机制具有重要意义。
Self-sustaining discharge activity of neuronal clusters is a widespread phenomenon in the brain that has been shown to be an important manifestation of behaviors such as working memory and goal orientation in the brain. The author constructs a hierarchical network simulation model with small-world features based on a non-linear integration-and-firing (IF) neuron model as a network node to study some characteristics of neuronal firing in self-sustaining activities. Under the appropriate model parameters, the hierarchical network can generate self-sustaining discharge activity, and its overall distribution frequency is relatively stable within 20 s after the external stimulus is removed. However, the internal distribution level is independent of the order of the layers. The overall frequency of the distribution tends to increase in the first positive relation and then reach saturation for the number of synaptic connections and the short path density. Meanwhile, the distribution frequency of the larger neuron networks is more sensitive to the short-path density. The results of this study are of great importance to reveal the mechanism of interaction between the functional nuclei of cerebral neurons.