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VII. WumanKinder: An Emergent Earthomo Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Fornito, Alex, et al. Competitive and Cooperative Dynamics of Large-Scale Brain Functional Networks Supporting Recollection. Proceedings of the National Academy of Sciences. 109/12788, 2012. University of Melbourne and Cambridge University behavioral neuroscientists further verify the presence of nature’s balanced complementarity of a relative agent-like neuronal or network semi-autonomy and a more relational modularity across cerebral anatomy and activities. Consider with Olaf Sporns 2012 herein who uses the phrase “segregation and integration” to similarly describe.

Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, context-dependent nature of large-scale brain dynamics and shed light on their contribution to individual differences in behavior. (Abstract)

Freeman, Walter. A Neurobiological Theory of Meaning in Perception. International Journal of Bifurcation and Chaos. 13/9, 2003. The first of a five part series, this article is subtitled: “Information and Meaning in Nonconvergent and Nonlocal Brain Dynamics.” Highly technical neuroscience which argues that the current emphasis on information processing misses the main activity going on: an organism’s attempt to make sense of its environment. In this regard, information and meaning might appear as left and right hemisphere complements.

The aim of this tutorial is to document a novel approach to brain function, in which the key to understanding is the capacity of brains for self-organization. (2493)

Friston, Karl. The History of the Future of the Bayesian Brain. NeuroImage. 62/1230, 2012. After being immersed for two decades in British and American neuroscience, the now Scientific Director of the Wellcome Trust Center for Neuroimaging surveys the discovery in those years of a cerebral dynamic self-organization, along with a cognitive faculty distinguished by an interactive responsiveness via hierarchical scales in congruence with its greater environment. Such a “Bayesian brain” is busy with optimizing its “beliefs” about any input or reply, so as to minimize any expense of “free energy.” As Friston speaks for the field, the approach, via “statistical physics and information theory,” can be seen to reveal another means to join human and universe.


Thomas Bayes (1701-1761) was a British mathematician and Presbyterian minister. Bayesian Statistics is a subset of the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief or, more specifically, Bayesian probabilities. Such an interpretation is only one of a number of interpretations of probability and there are many other statistical techniques that are not based on "degrees of belief". (Wikipedia)

The future of the Bayesian brain is clear: it is the application of dynamic causal modeling to understand how the brain conforms to the free energy principle. In this context, the Bayesian brain is a corollary of the free energy principle, which says that any self-organizing system (like a brain or neuroimaging community) must maximize the evidence for its own existence, which means it must minimize its free energy using a model of its world. Dynamic causal modeling involves finding models of the brain that have the greatest evidence or the lowest free energy. In short, the future of imaging neuroscience is to refine models of the brain to minimize free energy, where the brain refines models of the world to minimize free energy. This endeavor itself minimizes free energy because our community is itself a self organizing system. (Abstract, 1230)

This means that a Bayesian brain that tries to maximize its evidence is implicitly trying to minimize its entropy. In other words, it resists the second law of thermodynamics and provides a principled explanation for self organization in the face of a natural tendency to disorder. This means the Bayesian brain gracefully accommodates ensemble or population dynamics in evolutionary thinking within a statistical framework. In functionalist terms, such a self organizing system that minimizes its entropy would appear to be making Bayesian inferences about its sensory exchanges with the environment, which, of course, is just the Bayesian brain hypothesis. (1233)

Gazzaniga, Michael, ed. The New Cognitive Neurosciences. Cambridge: MIT Press, 2000. A large book by leading authorities covering a wide range of brain development, evolution, and cogitation.

Giusti, Chad, et al. Two’s Company, Three (or More) is a Simplex: Algebraic-Topological Tools for Understanding Higher-Order Structure in Neural Data. arXiv:1601.01704. University of Pennsylvania neuroscientists Giusti and Daniella Bassett, and mathematician Robert Ghrist, combine imaging techniques, network theories, and topological principles to press the frontiers of brain architecture studies. In other project postings this year they are joined by UPs Ann Sizemore (search), Edward Bullmore of Cambridge University, and others: Closures and Cavities in the Human Connectome (1608.03520), Classification of Weighted Networks through Mesoscale Homological Features Journal of Complex Networks (Online August, 2016), Small-World Brain Networks Revisited (1608.05665) and Multi-Scale Brain Networks at (1608.08828). Of further note, the paper advises a use of algebraic topologies, persistent homology, signs of universality, and so on to quantify cerebral faculties. And incredibly these exact phrases also appear in a concurrent, far removed posting by European astronomers about The Topology of the Cosmic Web (1608.0451. search Pranav). What great discovery of a cosmic connectome is arising in our midst?

The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad -- two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes, a theoretical notion developed in the field of mathematics known as algebraic topology, which is now becoming applicable to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. (1601.01704 Abstract)

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large and distributed networks of brain areas, examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates we move from considering exclusively pairwise interactions to capturing higher order relations, considerations naturally expressed in the language of algebraic topology. This provides architecture through which brain network can perform rapid, local processing. Complementary to this study of locally dense structures, we employ a tool called persistent homology to locate cycles, topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. (1608.03520 Abstract)

Grossberg, Stephen. Adaptive Resonance Theory: How a Brain Learns to Consciously attend, Learn, and Recognize a Changing World. Neural Networks. 37/1, 2013. In a lead article for the Twenty-fifth Anniversary Issue (see Kelso also), the Boston University computational neuroscientist, with colleague Gail Carpenter, updates the state of this insightful approach. Our life long neural capacity is seen as much engaged with learning and prediction, by virtue of “complementary cortical streams for attentional recognition and orienting action.” See also in this issue, “Essentials of the Self-Organizing Map” by its founder Teuvo Kohonen, and “Dreaming of Mathematical Neuroscience for a Half a Century” by the pioneer Japanese theorist Shun-ichi Amari.

Grossberg, Stephen. Linking Mind to Brain: The Mathematics of Biological Intelligence. Notices of the American Mathematical Society. 47/11, 2000. The Boston University neural network theoretician considers the deep principles that connect cerebral anatomy and physiology with dynamic streams of thought.

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)

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