摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-计算的新研究-神经计算是一篇报道的主题。根据NewsRx记者从法国萨克雷发回的新闻报道，研究表明，“平均场模型是计算神经科学中用于研究大量神经元行为的一类模型。这些模型的基础是将大量神经元的活动表示为平均场变量的平均行为。”新闻记者从巴黎-萨克雷大学获得了这项研究的引用。这种抽象使我们能够以一种计算效率高、数学上易于处理的方式研究大规模神经动力学。其中一种基于半解析方法的方法以前曾被应用于不同类型的单神经元模型，但从未应用于基于二次型的模型。在这项工作中，我们将这种方法应用于具有自适应和基于传导的突触相互作用的二次积分-激发神经元模型，并通过与尖峰网络模型的比较验证了平均场模型的有效性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Computation - Neural C omputation is the subject of a report. According to news reporting from Saclay, France, by NewsRx journalists, research stated, “Mean-field models are a class o f models used in computational neuroscience to study the behavior of large popul ations of neurons. These models are based on the idea of representing the activi ty of a large number of neurons as the average behavior of mean-field variables. ” The news correspondents obtained a quote from the research from Paris-Saclay Uni versity, “This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these meth ods, based on a semianalytical approach, has previously been applied to differen t types of singleneuron models, but never to models based on a quadratic form. I n this work, we adapted this method to quadratic integrate-and-fire neuron model s with adaptation and conductancebased synaptic interactions. We validated the mean-field model by comparing it to the spiking network model.”
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