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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Vazquez-Rodriguez, Bertha, et al. Stochastic Resonance at Criticality in a Network Model of the Human Cortex. Nature Scientific Reports. 7/13020, 2017. Universidad Nacional Autónoma de México, Indiana University, and Lausanne University Hospital neuroscientists including Olaf Sporns add significant neuroimage evidence for an innate propensity of human brains to situate cognitive activities in an optimal, critically poised state between noise and content. By so doing, our neural acuity becomes an ultra-complex exemplar of a uniVerse to human evolutionary genesis. See also The Signatures of Conscious Access and its Phenomenology are Consistent with Large-Scale Communication at Criticality by Enzo Tagliazucchi in Consciousness and Cognition (55/136, 2017).

Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain (connectome). The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality. (Abstract)

Growing evidence supports the hypothesis that the dynamics of the brain resembles the dynamics of a system near a critical point. This suggests that many functionally important features of brain dynamics may be optimized at criticality. Recent work has shown that a discrete state dynamical model implemented on a network of neuroanatomical connections exhibits a phase transition similar to that observed in a percolation model, where the average size of the second biggest cluster of active nodes reaches its maximum value for a
specific activation threshold. (1) In this work we determine quantitatively the amount of noise required for the best transmission of signals through the structural network of the brain’s connectome, and its relationship with the hypothesis of the brain operating near criticality. (2)

Vernon, David. Artificial Cognitive Systems. Cambridge: MIT Press, 2014. The University of Skovde, Sweden, professor of informatics provides a good update review and synthesis of the field and frontier of Cognitive Science. In this nascent paradigm, a cerebral, and robotic, cognizance is seen as emergent in some self-constructive way. The view can then join connectionist, dynamic systems theory, and enactive approaches, each of which is well explained. A “radical constructionism” is noted which does not deny prior representations but holds that an agent or entity proceeds to adapt and compose their own viable reality. If we might shift from microcosm to macrocosm, could we imagine a radical self-realizing uniVerse, of which peoples are the selves meant to achieve this?

Wang, Jilin, et al. Non-equilibrium Critical Dynamics of Bursts in θ and δ Rhythms as Fundamental Characteristic of Sleep and Wake Micro-architecture. PLoS Computational Biology. November, 2019. As the 2010s come to a close, in this Public Library of Science journal Boston University and UM Worcester Medical School researchers including Plamen Ivanov describe experiments and theories so as to report that even our daily resting phase is distinguished by independent, universal complex phenomena. Thus nighttime joins daylight wakefulness which, as Systems Neuroscience cites, prefers to be poised between more or less order. A person’s beingness then joins a quantum, universal complementarity which all other phases and site sections lately attest to. Once again microcosmic selves are vital exemplars of a macrocosmic genesis.

Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, challenge the current homeostatic framework for sleep regulation. Here we propose that a complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of θ-bursts (active phase) and exponential-like δ-bursts (quiescent phase) durations, which are typical features of non-equilibrium systems self-organizing at criticality. Importantly, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state, a solid indication of a basic principle in sleep dynamics. (Abstract excerpt)

The results demonstrate that critical dynamics underlie cortical activation during sleep and wake, and lay the foundation for a new paradigm, considering sleep micro-architecture as a non-equilibrium process and self-organization among neuronal assemblies to maintain a critical state, in contrast to the homeostasis paradigm of sleep regulation at large time scales. (Author Summary)

Power-law distributions are the statistical hallmark of scale invariance, and are typical features of physical systems at the critical point of a second order phase transition in equilibrium thermodynamics. At criticality systems exhibit high sensitivity to interactions among elements, leading to emergent collective behavior across scales, and thus, power laws. The critical point is located at the border between an ordered and a disordered phase, and can be reached by fine tuning external parameters. In contrast to this scenario, in non-equilibrium systems the dynamics can be spontaneously driven at criticality, where an active phase characterized by bursts/avalanches with power-law distributed sizes and durations coexists with a quiescent phase with exponential-like statistics. (3-4)

Wang, Yujiang et al. Neuro-evolutionary Evidence for a Universal Fractal Primate Brain Shape. . Eight neuro-researchers from Newcastle University, UK, University of Iowa, and University of Nottingham including Bruno Mota describe a frontier, multi-species study of cortex anatomies whose empirical sophistication are now found to be distinguished by a deep self-similar invariance. We then turn to a planatural philoSophia view to observe how a global brain faculty, as it continues these personal topologies and abilities, can be seen to reconstruct the hominid and animal brains that it altogether arose from.

The primate cerebral cortex can take on a wide diversity of shapes and sizes within species, whilst maintaining common qualities that make it a "brain". Here we present a new way to express the cortical shape as a structural hierarchy across spatial scales. In computational studies, as one removes sulci and gyri smaller than a specified scale, the cortices of 11 primate species are coarse-grained into less folded modes. As a result these cortices take on an archetypal fractal frame which implies a single universal gyrification on all folds. (Abstract)

Ultimately, we hope this new framework for expressing and analyzing cortical morphology, besides revealing a hitherto hidden regularity of nature, can become a powerful tool to characterize and compare cortices of different species and individuals, across development and aging, and across health and disease. (18)

Werner, Gerhard. Brain Dynamics across Levels of Organization. Journal of Physiology-Paris. 101/4-6, 2007. A University of Texas, Austin, physician employs “dynamic core” and “global neuronal workspace” models, with their dual “computational, connectivity spaces” of particular, subcortical processors and widespread, long range nets, to propose that these fluid neural phenomena are signatures of a robust self-organized criticality. As per the quote, a novel merging with statistical physics is advanced, which, one might add, can root which our exemplary cerebral cogitations into an implicate cosmos.

After initially presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the dynamic core (DC) and the global neuronal workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene–Changeux, on the other. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of self-organized criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG. (273)

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.

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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