VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality
2. Systems Neuroscience: Multiplex Networks and Critical Function
Guastello, Stephen, et al, eds. Chaos and Complexity in Psychology. Cambridge: Cambridge University Press, 2009. Annual conferences of the Society for Chaos Theory in Psychology & Life Sciences, and the pages of its journal Nonlinear Dynamics, Psychology, and Life Sciences, have for some years engaged the fertile application of complex system theories to personal behaviors and group communities. An understanding of their various facets are now sufficiently robust to be gathered in this large volume. Its compass includes an Introduction to Nonlinear Dynamics and Complexity wherein a fractal self-similarity, self-organized criticality, and so on, are found to provide an elusive theoretical explanation for our individual and social lives. (Which indeed offers another example of the natural universality of these genesis phenomena.) Chapters by Paul Van Geert, William Sulis, and Geoff Hollis, et al, are cited herein, along with especial contributions by Terrill Frantz and Kathleen Carley, David Pincus, and Peter Allen which span, e.g., agent-based modeling, psychotherapy, and organizations in evolution.
The theory of complex adaptive systems (CAS) describes the adaptive behavior of living systems as self-organizing, and…. can be used as an overarching framework to study the behavior of living organisms and to incorporate a broad variety of theoretical perspectives from biology, molecular genetics, physics, and chemistry into a single framework that deals in a very broad sense of self-organizing and adaptive behavior. Psychological processes quite naturally have their place within this framework. (Matthijs Koopmans, 521)
Guidolin, Diego, et al. Central Nervous System and Computation. Quarterly Review of Biology. 86/4, 2011. Some two decades since a “parallel distributed processing” or “connectivist” approach was taken up by cognitive science, University of Padova, University of Urbino, Karolinska Institute, and IRCCS San Camillo, neuroscientists survey its status, lately merging in translation with concurrent methods, that try to express how might brains compute thought, and by what digital and analog procedures. Similarly, see also Gualtiero Piccinini and Andrea Scarantino’s “Information Processing, Computation, and Cognition” in the Journal of Biological Physics (37/1, 2011). Both these papers have long bibliographies which widely cover this endeavor.
The brain, therefore, appears characterized by a peculiar combination of computational and noncomputational processes, and could be defined as a dynamically morphing system with computational capabilities of different types, undergoing genetically and environmentally driven self-organization in response to the external context. For this reason, some authors suggested that relevant conceptual frameworks provided by physics, such as statistical mechanics and nonlinear dynamics could represent for theoretical neuroscience particularly suitable tools, likely more helpful than the simple use of concepts from computability theory. (279-280)
Haddad, Wassim, ed. Entropy in Human Brain Networks. www.mdpi.com/journal/entropy/special_issues/brain-network. A topical 2015 collection with this title is edited by the Georgia Tech systems engineer in the online journal Entropy. The quote summarizes its content and aim. Among papers posted by July, we note Fractal Structure and Entropy Production within the CNS (search Seely), Applying Information Theory to Neuronal Networks by Thijs Jung, et al, and Human Brain Networks by Wassim Haddad, et al. In accord with many other disparate contributions this year, a grand unification of human and universe continues apace.
An important area of science where a dynamical system framework of thermodynamics can prove invaluable is in neuroscience. Nonlinear dynamical system theory, and in particular system thermodynamics, is ideally suited for rigorously describing the behavior of large-scale networks of neurons. Merging the two universalisms of thermodynamics and dynamical systems theory with neuroscience can provide the theoretical foundation for understanding the network properties of the brain by rigorously addressing large-scale interconnected biological neuronal network models that govern the neuroelectronic behavior of biological excitatory and inhibitory neuronal networks. As in thermodynamics, neuroscience is a theory of large-scale systems wherein graph theory can be a very useful tool in capturing the connectivity properties of system interconnections, with neurons represented by nodes, synapses represented by edges or arcs, and synaptic efficacy captured by edge weighting giving rise to a weighted adjacency matrix governing the underlying directed graph network topology. The purpose of this special issue is to use a dynamical systems framework merged with thermodynamic state notions to provide a fundamental understanding of the networks properties of the brain. (Synopsis)
Halford, Graeme, et al. Processing Capacity Defined by Relational Complexity. Behavorial and Brain Sciences. 21/4, 1998. Neural nets ought to be considered less in terms of bytes or agents and more about interconnections, their distributed processing. Once again this reciprocity characterizes a complex dynamical system.
He, Biyu Jade. Scale-Free Brain Activity: Past, Present, and Future. Trends in Cognitive Sciences. Online April, 2014. The NIH neuroscientist provides a succinct review of nature’s constant scale-invariance as dynamically evident in neural anatomy and thought. Her lab website (Google) notes research interests such as “Empirical and theoretical studies to elucidate the neurobiological nature and functional properties of scale-free brain activity.”
Brain activity observed at many spatiotemporal scales exhibits a 1/f-like power spectrum, including neuronal membrane potentials, neural field potentials, noninvasive electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) signals. A 1/f-like power spectrum is indicative of arrhythmic brain activity that does not contain a predominant temporal scale (hence, ‘scale-free’). This characteristic of scale-free brain activity distinguishes it from brain oscillations. Although scale-free brain activity and brain oscillations coexist, our understanding of the former remains limited. Recent research has shed light on the spatiotemporal organization, functional significance, and potential generative mechanisms of scale-free brain activity, as well as its developmental and clinical relevance. (Abstract)
He, Biyu, et al. Scale-free Dynamics and Critical Phenomena in Cortical Activity. Frontiers in Fractal Physiology. Online January, 2012. Biyu Jade He, NIH, with Andreas Daffertshofer, VU University, Amsterdam, and Tjeerd Boonstra, University of New South Wales, request contributions to a special issue on this topic, as systems neuroscience increasingly confirms that we personal microcosms indeed possess an archetypal macrocosmos in our brain/minds. See also He, et al, “The Temporal Structures and Functional Significance of Scale-free Brain Activity” in Neuron (66/3, 2010).
The brain is composed of many interconnected neurons that form a complex system, from which thought, behavior, and creativity emerge through self-organization. By studying the dynamics of this network, some basic motifs can be identified. Recent technological and computational advances have led to rapidly accumulating empirical evidence that spontaneous cortical activity exhibits scale-free and critical behavior. These findings may indicate that brain dynamics are always close to critical states – a fact with important consequences for how brain accomplishes information transfer and processing. Capitalizing on analogies between the collective behavior of interacting particles in complex physical systems and interacting neurons in the cortex, concepts from non-equilibrium thermodynamics can help to understand how dynamics are organized. In particular, the concepts of phase transitions and self-organized criticality can be used to shed new light on how to interpret collective neuronal dynamics. (Abstract)
Herculano-Houzel, Suzana, et al. Mammalian Brains are Made of These. Brain, Behavior and Evolution. 86/3-4, 2015. A Dataset of the Numbers and Densities of Neuronal and Nonneuronal Cells in the Brain of Glires, Primates, Scandentia, Eulipotyphlans, Afrotherians and Artiodactyls, and Their Relationship with Body Mass is the long subtitle. A neuroscientist team from Argentina, the United States, and South Africa including Jon Kaas, contribute to mid 2010s advances as our worldwide sapiensphere proceeds to reconstruct and quantify the organismic cerebral encephalization this cognitive ability arose from.
A Dataset of the Numbers and Densities of Neuronal and Nonneuronal Cells in the Brain of Glires, Primates, Scandentia, Eulipotyphlans, Afrotherians and Artiodactyls, and Their Relationship with Body Mass is the long subtitle. A neuroscientist team from Argentina, the United States, and South Africa including Jon Kaas, contribute to mid 2010s advances as our worldwide sapiensphere proceeds to reconstruct and quantify the organismic cerebral encephalization this cognitive ability arose from.
Ishikawa, Masumi, et al, eds. New Developments in Self-Organizing Systems. Neural Networks. 17/8-9, 2004. A large edition dedicated to advances in understanding the brain’s dynamic, hierarchical formation and performance.
Ju, Harang and Danielle Bassett. Dynamic Representations in Networked Neural Systems. Nature Neuroscience. 23/8, 2020. Akin to Muhua Zheng, et al below, University of Pennsylvania neuroscientists delve deeper into our cerebral endowment to find more consistent, layered repositories of knowing inputs and response. Each paper cites dozens of prior references as our collective, 21st project of retrospective self-quantification proceeds to reveal a macro-uniVerse to micro-wumanVerse familial correspondence.
Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these approaches. Here we review the two separate bodies of literature; we next note progress made to join them. We then discuss how patterns of activity evolve from one representation to another, forming dynamic content that unfolds on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission along with exciting frontiers for future research. (Abstract excerpt)
Kahn, D., et al. Dreaming and the Self-Organizing Brain. Journal of Conscious Studies. 7/7, 2000. The authors contend that conceiving the brain as a fractally scaled, critically poised system can bring new understandings and explanations for dream activity and content.
Kaiser, Marcus. A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks. NeuroImage. 57/3, 2011. In an issue on Educational Neuroscience, a Newcastle University neuroinformatics specialist provides an overview of this systems turn to study pervasive interconnections from local neuron and net to global cerebration. And we enter as an example, mid 2011, of how much this “–omics” view is accepted across every natural, organismic, and social realm. However might we imagine a “cosmic connectome” whence the whole genesis uniVerse, or “cosmome” (putting “mom” back again), be rightly appreciated as the animate, generative essence it is?
High-throughput methods for yielding the set of connections in a neural system, the connectome, are now being developed. This tutorial describes ways to analyze the topological and spatial organizations of the connectome at the macroscopic level of connectivity between brain regions as well as the microscopic level of connectivity between neurons. We will describe topological features at three different levels: the local scale of individual nodes, the regional scale of sets of nodes, and the global scale of the complete set of nodes in a network. Such features can be used to characterize components of a network and to compare different networks, e.g. the connectome of patients and control subjects for clinical studies. At the global scale, different types of networks can be distinguished and we will describe Erdös–Rényi random, scale-free, small-world, modular, and hierarchical archetypes of networks. Finally, the connectome also has a spatial organization and we describe methods for analyzing wiring lengths of neural systems. As an introduction for new researchers in the field of connectome analysis, we discuss the benefits and limitations of each analysis approach. (Abstract)
Kaiser, Marcus, et al. Hierarchy and Dynamics of Neural Networks. Frontiers in Neurodynamics. 4/Article 112, 2010. Amongst this burst of cyberscience communications, an Introduction to a special collection that adds further mature credence to how our cerebral anatomy and cognition is formed and functions by self-organizing complexities.
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