Brain works principle followed by neural information processing: a review of novel brain theory

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rubin Wang, Yihong Wang, Xuying Xu, Yuanxi Li, Xiaochuan Pan
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引用次数: 3

Abstract

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

大脑工作原理遵循神经信息处理:新的大脑理论综述
大脑的工作方式及其工作原理长期以来一直是科学家梦寐以求解决的重大科学问题。然而,众所周知,大脑在不同的层次上工作,不同层次上的运作是相互影响、相互耦合的。不幸的是,直到现在,我们仍然不知道不同层次的神经系统是如何相互作用和耦合的。这篇综述提供了一些关于如何解决这些科学问题的初步讨论,为此我们提出了一种新的大脑理论,称为神经能量。这种理论和研究方法可以将神经信息与神经能量结合起来,以解决神经系统在各个层面上的相互作用。因此,本文系统总结了我们在脑科学领域研究中提出的神经能理论和方法,以及力学与神经能理论的内在联系。重点讨论了如何利用分析动力学的思想构建一个等价于霍奇金-赫胥黎(H-H)模型的Wang-Zhang (W-Z)神经元模型。在此基础上,提出了神经科学领域的大尺度神经模型和大脑全局神经编码的理论框架。包括视觉感知等多个感觉和知觉神经系统的信息处理、大脑默认模式网络与功能网络耦合的神经机制、记忆切换与大脑状态切换、大脑导航、神经元工作新机制的预测、神经科学难以解释的实验现象的解释等。事实证明,新的W-Z神经元模型和神经能量理论在神经建模、神经信息处理和方法上具有独特的功能和优势。以神经能量为核心的大规模神经科学研究思路,将为未来促进实验神经科学与理论神经科学的融合提供潜在的有力研究方法,并在实验神经科学与理论神经科学之间提出一个被广泛接受的脑理论体系。抛弃还原论和整体论研究方法在神经科学领域的不足,有效整合各自在方法论上的优势,具有重要的科学意义。
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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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