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24 pages, 1723 KiB

Open AccessReview

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

by Kevin B. Clark

Biology 2023, 12(3), 352; https://doi.org/10.3390/biology12030352 - 23 Feb 2023

Cited by 2 | Viewed by 3488

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. Full article

(This article belongs to the Special Issue New Era in Neuroscience)

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15 pages, 2116 KiB

Open AccessArticle

Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

by Yicheol Han, Stephan J. Goetz and Claudia Schmidt

Sustainability 2021, 13(3), 1512; https://doi.org/10.3390/su13031512 - 1 Feb 2021

Cited by 5 | Viewed by 5867

Abstract

This article presents a spatial supply network model for estimating and visualizing spatial commodity flows that used data on firm location and employment, an input–output table of inter-industry transactions, and material balance-type equations. Building on earlier work, we proposed a general method for visualizing detailed supply chains across geographic space, applying the preferential attachment rule to gravity equations in the network context; we then provided illustrations for U.S. extractive, manufacturing, and service industries, also highlighting differences in rural–urban interdependencies across these sectors. The resulting visualizations may be helpful for better understanding supply chain geographies, as well as business interconnections and interdependencies, and to anticipate and potentially address vulnerabilities to different types of shocks. Full article

(This article belongs to the Special Issue Rural and Urban Management: Innovative Strategies to Enhance Resilience)

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