Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks.
Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks.
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佩索阿假设，与处理和表达充满情感的内容（如杏仁核和边缘皮质）相关的麸皮解剖学是资源容量有限的。因此，大脑需要多通道或平行的结构-功能连接来有效地感知、激励、整合、表征、回忆和执行认知-情感关系。Pessoa使用2D图网络理论来支持他关于分布式大脑组织和操作的观点，得出的结论是大脑通过双进程竞争和合作来形成高度嵌入的计算架构，几乎没有结构-功能划分。低维图论已经成为一种流行的数学工具，用于建模，模拟和可视化不断发展的复杂，有时难以处理的大脑网络。图论为研究和理解各种生物和技术网络行为提供了优势，对于Pessoa来说，它允许一个框架，该框架解释了迄今为止可能被“传统”观点解释得很差的结构-功能特征，这些观点主张将结构-功能关系映射到定位良好的大脑区域。然而，佩索阿未能充分认识到脑动力学的弱到强的结构-功能相关性的重要性，以及为什么这些由赫布和反赫布神经元可塑性等微分控制参数引起的相关性最好用神经场理论来评估。神经领域表明，嵌入式大脑网络最佳的异国情调的计算阶段和连续的限制与一些网络分区的伴奏，而不是无约束的嵌入，渲染健康的认知情感功能之间的演变。在《认知-情绪大脑》一书中，佩索阿通过回避神经场理论和代表神经元可塑性的生理学衍生结构，忽视了连续体对非线性大脑网络连接的影响。缺乏这些内容，对于理解大脑的动态结构-功能嵌入和分区非常重要，削弱了神经网络丰富的竞争和合作性质，并使Pessoa的论点以及其他作者的类似论点变得微不足道，这些论点是关于充满可变强度神经连接的最佳整合大脑的系统发育和操作意义。黎曼神经流形，包含限制强加化生赫布和反赫布型控制变量，模拟可扩展的网络行为，这是很难从佩索阿和其他神经科学家更喜欢的简单图论分析中捕获的。场论表明，嵌入式认知情感网络的分区和性能优势，最佳地在异国情调的经典和量子计算阶段之间进化，其中矩阵奇异性和凝聚产生退化的结构-功能同质性，这对健康的大脑来说是不现实的。因此，为了有效地执行认知-情绪网络功能，需要对网络进行一定的划分，而不是不受约束的嵌入，在我们的神经科学新时代，这应该被认为是正确的大脑组织和运作的一个关键方面。
Pessoa postulates that bran anatomy associated with the processing and expression of emotion-laden content, such as the amygdala and limbic cortices, is resource capacity-limited. Thus, brains require multichannel or parallel structure-function connectivity to effectively perceive, motivate, integrate, represent, recall, and execute cognitive-emotional relationships. Pessoa employs 2D graph network theory to support his views on distributed brain organization and operation, concluding that brains evolve through dual-process competition and cooperation to form highly embedded computational architectures with little structure–function compartmentalization. Low-dimensional graph theory has become a popular mathematical tool to model, simulate, and visualize evolving complex, sometimes intractable, brain networks. Graph theory offers advantages to study and understand various biological and technological network behaviors and, for Pessoa, it permits a framework that accounts for structure–function features thus far poorly explained by perhaps “traditional” perspectives, which advocate for the mapping of structure–function relationships onto well-localized brain areas. Pessoa nonetheless fails to fully appreciate the significance of weak-to-strong structure-function correlations for brain dynamics and why those correlations, caused by differential control parameters such as Hebbian and antiHebbian neuronal plasticity, are best assessed using neural field theories. Neural fields demonstrate that embedded brain networks optimally evolve between exotic computational phases and continuum limits with the accompaniment of some network partitioning, rather than unconstrained embeddedness, when rendering healthy cognitive-emotional functionality. In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.
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