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A Game-Theoretical Network Formation Model for C. elegans Neural Network

机译:秀丽隐杆线虫神经网络的博弈论网络形成模型

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摘要

Studying and understanding human brain structures and functions have become one of the most challenging issues in neuroscience today. However, the mammalian nervous system is made up of hundreds of millions of neurons and billions of synapses. This complexity made it impossible to reconstruct such a huge nervous system in the laboratory. So, most researchers focus on C. elegans neural network. The C. elegans neural network is the only biological neural network that is fully mapped. This nervous system is the simplest neural network that exists. However, many fundamental behaviors like movement emerge from this basic network. These features made C. elegans a convenient case to study the nervous systems. Many studies try to propose a network formation model for C. elegans neural network. However, these studies could not meet all characteristics of C. elegans neural network, such as significant factors that play a role in the formation of C. elegans neural network. Thus, new models are needed to be proposed in order to explain all aspects of C. elegans neural network. In this paper, a new model based on game theory is proposed in order to understand the factors affecting the formation of nervous systems, which meet the C. elegans frontal neural network characteristics. In this model, neurons are considered to be agents. The strategy for each neuron includes either making or removing links to other neurons. After choosing the basic network, the utility function is built using structural and functional factors. In order to find the coefficients for each of these factors, linear programming is used. Finally, the output network is compared with C. elegans frontal neural network and previous models. The results implicate that the game-theoretical model proposed in this paper can better predict the influencing factors in the formation of C. elegans neural network compared to previous models.
机译:研究和理解人脑的结构和功能已经成为当今神经科学中最具挑战性的问题之一。但是,哺乳动物的神经系统由数亿个神经元和数十亿个突触组成。这种复杂性使得不可能在实验室中重建如此庞大的神经系统。因此,大多数研究人员专注于秀丽隐杆线虫神经网络。秀丽隐杆线虫神经网络是唯一被完全映射的生物神经网络。这个神经系统是存在的最简单的神经网络。但是,许多基本行为(例如运动)都从此基本网络中出现。这些特征使秀丽隐杆线虫成为研究神经系统的方便案例。许多研究试图提出线虫神经网络的网络形成模型。但是,这些研究不能满足秀丽隐杆线虫神经网络的所有特征,例如在秀丽隐杆线虫神经网络的形成中起作用的重要因素。因此,需要提出新的模型以解释秀丽隐杆线虫神经网络的所有方面。为了理解影响线虫额叶神经网络特征的神经系统形成的因素,本文提出了一种基于博弈论的新模型。在此模型中,神经元被认为是代理。每个神经元的策略包括建立或删除与其他神经元的链接。选择基本网络后,将使用结构和功能因素来构建效用函数。为了找到每个因素的系数,使用了线性编程。最后,将输出网络与秀丽隐杆线虫额叶神经网络和以前的模型进行比较。结果表明,与以前的模型相比,本文提出的博弈论模型可以更好地预测线虫神经网络形成的影响因素。

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