VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Werner, Gerhard. Fractals in the Nervous System: Conceptual Implications for Theoretical Neuroscience. Frontiers in Fractal Physiology. 1/Article 15, 2010. With regard to the Frontiers international peer process, the article editor was Dante Chialvo, and reviewers are Henrik Jensen and Matias Palva. A growing realization is that bodily anatomy and physiology across circulation, the CNS, respiration, metabolic webs, and other domains, is distinguished by an optimally efficient self-similarity, This extensive paper further explains, supported by much documentation, that our cerebral topology and thought are likewise graced by dynamic, power law, scale-free networks, and self-organized criticalities. By this advance, deep connections can be made with the complex phenomena that statistical physics studies. A constant analogy appears in this work, that body and mind exemplify nested Russian Matryoshka dolls. repetition. In which case, as we often note, serves to reveal nature�s human genesis universe. See also by GW: "Consciousness Viewed in the Framework of Brain Phase Space dynamics, Criticality, and the Renormalization Group" in Chaos, Solitons & Fractals, online October 2012.

Werner, Gerhard. Metastability, Criticality and Phase Transitions in Brain and its Models. BioSystems. 90/2, 2007. The University of Texas at Austin neuroscientist employs statistical physics as another way to appreciate the same scaling and universality properties in cerebral cognition that appear everywhere else in cosmic to human nature. We quote its Abstract.

This survey of experimental findings and theoretical insights of the past 25 years places the brain firmly into the conceptual framework of nonlinear dynamics, operating at the brink of criticality, which is achieved and maintained by self-organization. It is here the basis for proposing that the application of the twin concepts of scaling and universality of the theory of non-equilibrium phase transitions can serve as an informative approach for elucidating the nature of underlying neural-mechanisms, with emphasis on the dynamics of recursively reentrant activity flow in intracortical and cortico-subcortical neuronal loops. (496)

Wilkerson, Galen, et al. Spontaneous Emergence of Computation in Network Cascades. arXiv:2204.11956.. Senior complexity scholars GW and Henrik Jensen, Imperial College London and Sotiris Moschoyiannia, University of Surrey, UK post a theoretical exercise as they join a growing endeavor to model an apparent procreative genesis with an innate propensity to form and process an informational essence. A retrospect survey of 44 references from A. Turing, J. A. Wheeler and S. Wolfram to this worldwise phase quite evinces how the past decades have been a long encounter with a radical reality which seems to be trying to quantify and express itself. That is to say, so to achieve its own internal, quantified self-description and observant recognition.

Computation by neuronal networks and by avalanche supporting networks are of interest to the fields of physics, computer science as well as machine learning. Here we show that computations of complex Boolean functions arise spontaneously in threshold networks as a function of connectivity and inhibition via logic automata (motifs) in the form of computational cascades. We also show that the optimal fraction of inhibition observed supports results in neuroscience, relating to optimal information processing. (Excerpt)

A somewhat surprising initial result in this investigation is that complex functions on inputs emerge spontaneously and seemingly inevitably as threshold networks are connected at random. (2)

The relationship between physical systems and information has been of increasing and compelling interest in the domains of physics, neuroscience, computer science, quantum computing, and other fields such as computation in social networks, or biology to the point where some consider information to be a fundamental phenomenon in the universe. Often, physical systems operating on information take place on, or can be modeled by, network activity, since information is transmitted and processed by interactions between physical entities. (2)

Willshaw, David. Self-organization in the Nervous System. Morris, Richard, et al, eds. Cognitive Systems: Information Processing Meets Brain Science. Amsterdam: Elsevier, 2006. The University of Edinburgh computational neurologist discusses at length how the local interaction of many neuronal entities or elements, along with external influences, can organize themselves into cerebral development, effectively respond to experiential change, and can correct to damaging effects.

Xu, Yiben, et al.. Interacting spiral wave patterns underlie complex brain dynamics related to cognitive processing. Nature Human Behavior. 7/1196, 2023. University of Sydney and Fudan University, China proceed to identify and explain an occasion of cerebral thoughts and responses as they seem to spin themselves.

The large-scale activity of the human brain exhibits rich and complex patterns, but their spatiotemporal dynamics in cognition remain unclear. Here we study fluctuations of human cortical magnetic resonance imaging signals, and show that spiral-like, rotational wave patterns are widespread during both resting and cognitive tasks. We demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions. Our findings suggest that rotational patterns organize complex neural dynamics and have functional correlates to cognitive processing. (Excerpt)

Yufik, Yan and Karl Friston. Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems. Frontiers of Systems Neuroscience. December, 2016. A contribution to our sapient retrospective of how creaturely and human brains became collectively able to achieve this. See also in this journal Coordination Dynamics in Cognitive Neuroscience by Steven Bressler and Scott Kelso (September 2016). Both papers and others in this edition, and other Frontiers (Google) online journals offer more links in regard. As a capsule, our neural endowments now known to be critically poised, dynamic multiplex network systems with vast capabilities to learn and create, quite a microcosmic exemplar of the macroscopic genesis universe.

Zamora-Lopez, Gorka, et al. Characterizing the Complexity of Brain and Mind Networks. Philosophical Transactions of the Royal Society A. 369/3730, 2011. In a focus issue on “The Complexity of Sleep,” this lead article by German, Italian, Argentinean, and Chinese neuroscientists draws upon the nascent maturity of nonlinear theories to show how even brain anatomy and function, along with linguistic affairs, similarly and robustly exhibit these ubiquitous patterns and processes. See also the introductory tutorial “The Sleeping Brain as a Complex System” by Eckehard Olbrich, Peter Achermann, and Thomas Wennekers.

Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments. (3730)

Networks usually contain distinguishable groups of nodes, named modules or communities. The nodes within a module are densely connected to each other, but less likely connected to nodes in other modules. In a similar manner in which nodes group into modules, the modules can also join to form larger modules, giving rise to hierarchically nested structures. (3732) Uncovering the organization of both the brain and mind is a difficult experimental quest, but the last two decades have seen several advances on both sides, particularly, since concepts and tools of complex networks have been applied to study their organization. Here, we have reviewed those findings and concluded that both neural and language-related networks share relevant common properties: (i) a broad degree distribution containing hubs, (ii) small-world properties, and (iii) an organization into modular and hierarchical structures. (3743)

Zheng, Mengsen, et al. Topological Portraits of Multiscale Coordination Dynamics. arXiv:1909.08809. Center for Complex Systems and Brain Sciences, Florida Atlantic University, researchers MZ, William Kalies, Scott Kelso and Emmanuelle Tognoli (search SK and ET) continue their studies of metastable systems in the case of our daily human interactivities. The advance herein is a novel ability to create graphic visualizations, which notably are reached by the same simplicial complex, persistent homology, and algegraic topology methods as used by neuroscientist to reveal how the brain works (Danielle Bassett). A Topological Recurrence Plots Reveal Structures in Complex Coordination Patterns section implies the presence of an independent mathematics and geometry which manifest itself everywhere.

Living systems exhibit complex yet organized behavior on multiple spatiotemporal scales. To investigate multiscale coordination in living systems, one needs to quantify the complex dynamics in both theoretical and empirical realms. This work shows how integrating approaches from computational algebraic topology and dynamical systems may help meet this challenge. First, we argue why certain topological features and their scale-dependency are relevant to understanding complex collective dynamics. We then propose a way to capture topological information using persistent homology. Finally, the method is tested by detecting transitions from experimental rhythmic coordination in ensembles of interacting humans. Such multiscale portraits highlight collective aspects of coordination patterns that are irreducible to individual parts. (Abstract excerpt)

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