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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Kirchhoff, Michael. Predictive Brains and Embodied, Enactive Cognition: An Introduction to the Special Issue. Synthese. 195/6, 2018. An editorial for a Special Issue on Predictive Brains and Embodied, Enactive Cognition by the University of Wollongong, Australia philosopher which reviews and assimilates these dynamic neuroscientific, thought-provoking vistas. See also his own entry Autopoiesis, Free Energy, and the Life-Mind Continuity Thesis which continues to join these schools which are traced back to the work of Francisco Varela, see the second Abstract.

All the papers in this special issue intersect work on predictive processing models in the neurosciences and embodied, enactive perspectives on mind. All contributions deal with questions of whether and how key assumptions of the predictive brain hypothesis can be reconciled with approaches to cognition that take embodiment and enaction as playing a central and constitutive role in our cognitive lives. While there is broad consensus that bodily and worldly aspects matter to cognition, predictive processing is often understood in epistemic, inferential and representational terms. Rather than stressing how these accounts differ, others such as Andy Clark (University of Edinburgh) emphasize what they have in common, focusing on how predictive processing models provide “the perfect neuro-computational partner for work on the embodied mind.” Our aim is to nudge this particular area of research forward by examining how to combine the best of these frameworks in a joint pursuit. (Intro Abstract excerpt)

The life–mind continuity thesis is difficult to study, especially because the relation between life and mind is not yet fully understood, and given that there is still no consensus view neither on what qualifies as life nor on what defines mind. This paper considers two influential accounts addressing how best to understand the life–mind continuity thesis: first, the theory of autopoiesis (AT) developed in biology and in enactivist theories of mind; and second, the recently formulated free energy principle in theoretical neurobiology, with roots in thermodynamics and statistical physics. This paper advances two claims. The first is that the free energy principle (FEP) should be preferred to the theory of AT, as classically formulated. The second is that the FEP and the recently formulated framework of autopoietic enactivism can be shown to be genuinely continuous on a number of central issues, thus raising the possibility of a joint venture. (Autopoiesis Abstract)

Knyazeva, Helena. Nonlinear Cobweb of Cognition. Foundations of Science. 14/3, 2009. The Evolutionary Epistemology program director at the Institute of Philosophy of the Russian Academy of Sciences here proposes to recast cerebral activities, both for persons and contextual nature, in terms of complex system phenomena. As a result, a constructivist, enactive emergence appears as a “coming to be” self-realization for both individuals and encompassing cosmos, each engaged in a vast learning process.

Cognition is dynamical and under construction in the processes of self-organization. In other words, cognitive systems are dynamical and self-organizing ones. The functioning of cognitive systems is similar in principle to the function of natural systems which undergo our cognition, i.e., of objects of the surrounding world, the essence of there processes is analogous. Therefore, in the frames of the embodied cognition approach called also the dynamical approach in cognitive science, the latest developments in the fields of nonlinear dynamics, the theory of complex adaptive systems, the theory of self-organized criticality and synergetics are widely and fruitfully used. (171)

The autopoietic nature of cognition lies in its ability for self-completing of integral images, perceptible and mental pictures. The image of self-completing of a whole cognitive structure is similar to the growth of “tree of knowledge” on a specially prepared and cultivated field of consciousness. It is a matter of a certain analogue of the biological process of morphogenesis. (175)

Koch, Christoph and Gilles Laurent. Complexity and the Nervous System. Science. 284/96, 1999. A survey article on the explanatory value of dynamical theories in neuroscience.

While everyone agrees that brains constitute the very embodiment of complex adaptive systems and that Albert Einstein’s brain was more complex than that of a housefly, nervous system complexity remains hard to define.…Any realistic notion of brain complexity must incorporate, first the highly nonlinear, nonstationary and adaptive nature of the neuronal elements themselves and, second, their nonhomogeneous and massive parallel patterns of interconnections whose ‘weights’ can wax and wane across multiple time scales in behaviorally significant ways. (98)

Kozma, Robert and Walter Freeman. Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems. Frontiers of Systems Neuroscience. Online February, 2017. The University of Memphis mathematician is here joined by the late (1927-2016) UC Berkeley pioneer theorist of our cerebral activity by the grace of nonlinear complexities. We cite for its content, and as an exemplar of nature’s tendency to reside in a critically poised optimum state between too much chaos or order. A middle balance of these reciprocal complements, as perennial wisdom teaches, is ever best. A worst out-of-kilter situation might then be American politics where they are locked in mutual destruction.

Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We employ random graph theory and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli. (Abstract excerpts)

Kwisthout, Johna, et al. Special Issue on Perspectives on Human Probabilistic Inference and the Bayesian Brain. Brain and Cognition. 112/1, 2017. An issue editorial for a collection of papers broadly about the predictive brain theory of Karl Friston and many colleagues. See for example, The Infotropic Machine, A Social Bayesian Brain, and Explanatory Pluralism.

Lake, Blue, et al. Neuronal Subtypes and Diversity Revealed by Single-Nucleus RNA Sequencing of the Human Brain. Science. 352/1586, 2016. We cite this contribution cited by the journal as Neurogenomics by a 21 member team at UC San Diego and the Scripps Institute as a good example of how genomic and cerebral (neuromics?) studies have come to employ and share similar techniques and vernaculars, as if the same phenomena. See also Canonical Genetic Signatures of the Adult Human Brain and Transcriptional Architecture of the Human Brain, both in Nature Neuroscience (18/12, 2015). And at this point, we would like to make notice that other domains such as linguistics, cosmic webs, quantum systems, are also adopting sequence techniques, as common genetic code becomes evident everywhere.

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain. (Abstract)

Le Van Quyen, Michel. The Brainweb of Cross-scale Interactions. New Ideas in Psychology. 29/2, 2011. In similar, independent work to Herculano-Houzel and colleagues in Brazil, a Université Pierre et Marie Curie, Paris, neuroscientist describes a multi-level cerebral cognition from neuron and synapse to whole brain nets that exhibits both upward and downward causation, from which then arises conscious awareness. Upon reflection, one gets impression in each of these cases, of a spatial and temporal microcosm in our own heads of a macrocosmic genesis just ascending to conscious individuation through the human phenomenon.

From neuron to behaviour, the nervous system operates on many levels of organization, each with its own scales of time and space. Very large sets of data can now be obtained from these multiple levels by the explosive growth of new physiological recording techniques and functional neuroimaging. Among the most difficult tasks are those of conceiving and describing the exchanges between levels, seeing that the scales of time and distance are braided together in a complex web of interactions, and that causal inference is far more ambiguous between than within levels. In this paper, I propose that a generic description of these multi-level interactions can be based on the temporal coordination of neuronal oscillations that operate at multiple frequencies and on different spatial scales. (Abstract)

Levina, Anna, et al. Dynamical Synapses Causing Self-Organized Criticality in Neural Networks. Nature Physics. 3/857, 2007. With co-authors Michael Herrmann and Theo Geisel, a progress report on realizations that our brains are in fact, a revelatory epitome of a quickening, awakening, individuating, genesis cosmos.

Self-organized criticality is one of the key concepts to describe the emergence of complexity in natural systems. The concept asserts that a system self-organizes into a critical state where system observables are distributed according to a power law. Prominent examples of self-organized critical dynamics include piling of granular media, plate tectonics and stick–slip motion. Critical behaviour has been shown to bring about optimal computational capabilities, optimal transmission, storage of information and sensitivity to sensory stimuli. In neuronal systems, the existence of critical avalanches was predicted and later observed experimentally. Here, we demonstrate analytically and numerically that by assuming (biologically more realistic) dynamical synapses in a spiking neural network, the neuronal avalanches turn from an exceptional phenomenon into a typical and robust self-organized critical behaviour, if the total resources of neurotransmitter are sufficiently large.

Li, Mike, et al. Transitions in Information Processing Dynamics at the Whole-Brain Network Level are Driven by Alterations in Neural Gain. PLoS Computational Biology. October, 2019. University of Sydney and Stanford University neuroscientists once more well quantify how cerebral processes perform at their best by a reciprocity of relative separations and whole assemblies.

Dynamic brain complexity can adapt its functional network structure between integrated and segregated states in response to different cognitive tasks. In this study, we show that the dynamics of the subcritical (segregated) regime are involved with information storage, while the supercritical (integrated) phase is more associated with information transfer. Operating near to the critical state by modulating neural gain parameters thus appears to provide computational advantages and flexibility in information processing. (Abstract excerpt)

Liang, Junhao and Changsong Zhou. Criticality Enhances the Multilevel Reliability of Stimulus Responses in Cortical Neural Networks. PLOS Computational Biology.. January, 2022. We cite this entry by Beijing-Hong Kong-Singapore, Joint Centre for Nonlinear and Complex Systems researchers for one more notice of nature’s prime preference to seek and reside at an optimum balance between two opposite but reciprocal states. Our especial is a tendency toward Chimera-like active or static modes.

The complexity of dynamical brain activity ranges from neuronal spiking and neural avalanches to oscillatory potentials of local circuits across many states. Such multilevel variables are functionally and behaviorally relevant. To better clarify, we study the stimulus–response of neural circuits. Our model assumes excitation–inhibition (E–I) interactions and synaptic couplings with a critical sub-region. We further analyze the nonlinear dynamical principles using a novel and a broadly applicable mean-field theory. (Summary excerpt)

Lindsay, Grace. Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.. London: Bloomsbury Sigma, 2021. An Assistant Professor of Psychology and Data Science at New York University begins her book with a review of mathematic and physical principles so they can be applied to cognitive functions such as memories, vision, decision making, excitation/inhibition. With this currency in place, some Grand Unified Theories are surveyed such as Karl Friston’s free energy, Jeff Hawkins’ Thousand Brains project, and Giulio Tononi’s integrated information model. And one wonders whomever is this late planetary faculty as it proceeds to learns on its own. What manner of multiuniverse needs to form a midway self-representation, realization and participatory affirmation?

In Models of the Mind, computational neuroscientist Grace Lindsay explains how mathematical models have allowed researchers to understand and describe many of the brain's processes such as decision-making, sensory processing, stored memory, and more. Each chapter focuses on mathematical tools that have been applied from the individual neuron to their many interactions, whole brain areas and the consequent behaviours. In addition, Grace examines the history of the field from the eighteenth century and to the large models of neural networks that form the basis of modern artificial intelligence.

Lofti, Nastaran, et al. Statistical Complexity is Maximized Close to Criticality in Cortical Dynamics. arXiv:2010.040123. Nine Brazilian neuroscientists contribute to a growing notice that cerebral activity tends and prefers to reside in this optimum balance. See also Quasicritical Brain Dynamics by Leandro Fosque, et al at 2010.02938 and Testing the Critical Brain Hypothesis using a Phenomenological Renormalization Group by Giorgio Nicoletti, et al at 2001.04353 for further work.

Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity to desynchronized and disordered regimes. It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. We show that statistical complexity is maximized close to criticality for cortical spiking data, as well as for a network model of excitable elements at a critical point of a non-equilibrium phase transition. (Abstract excerpt)

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