网络模型中的信息流动与边际收益递减规律
Information flow in a network model and the law of diminishing marginal returns
摘要：
我们分析了一个简单的动态网络模型，它描述了有限的能力，节点处理输入信息。对于一个合适的选择的参数，信息流模式的特点是指数分布的传入信息和厚尾分布的传出信息，作为边际收益递减规律的签名。一个真实的EEG数据集的分析表明，类似的现象可能是相关的大脑信号。
英文摘要：
We analyze a simple dynamical network model which describes the limited capacity of nodes to process the input information. For a suitable choice of the parameters, the information flow pattern is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. The analysis of a real EEG data-set shows that similar phenomena may be relevant for brain signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
