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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VII. WumanKinder: An Emergent Earthomo Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Nicolelis, Miguel and Sidarta Ribeiro. Seeking the Neural Code. Scientific American. December, 2006. Presently at Duke University, the authors are co-founders of the new International Institute of Neuroscience of Natal, Brazil. A good survey by prolific researchers on the transition from an emphasis in this field on point neurons to their contextual place and role in avalanches of pulsing electrical networks.

Nolfi, Stefano, et al. Behavior and Mind as a Complex Adaptive System. Adaptive Behavior. 16/2-3, 2008. An introduction to a special issue on such perceptions of a nested cerebral and cognitive self-organization. See also online a survey article on this approach by Nolfi in Complexus (2/3-4, 2005). One more take that human and universe embody, embrain and dynamically mirror each other.

Nunez, Paul and Ramesh Srinivasan. Hearts Don’t Love and Brains Don’t Pump: Neocortical Dynamic Correlates of Conscious Experience. Journal of Consciousness Studies. 14/8, 2007. Neuroscientists from Tulane University and UC Irvine illustrate their views on how the brain functions by way of a grand analogy. The same self-organizing, synergetic, hierarchical complexity is said to characterize both neural and social systems. Just as the brain is dynamically arrayed from neuron to neocortex, so civilizations are from individuals to global populations. Cerebral consciousness arises from resonantly tuned networks and synaptic fields. The article doesn’t go there, but it implies that our ultra-intricate global society, with a similar number of people as the brain has neurons, as Peter Russell has noted, ought to possess its own cognitive capabilities and knowledge, which is the basic premise of this website.

For genuine scientific reasons that are independent of our sociological metaphor, it appears that two general features of brain tissue are especially important in healthy brains, hierarchical interactions and non-local interactions. These properties are also important characteristics of the human global social system. The cooperation and conflict between individuals, cities, nations and so forth serves as a convenient metaphor for neural interactions at multiple scales. (22)

O’Brien, Gerard and Jonathan Opie. A Connectionist Theory of Phenomenal Experience. Behavioral and Brain Sciences. 22/1, 1999. Connectionism often connotes the apply of dynamical systems theory to cerebral activities.

Phenomenal experience consists of the explicit representation of information in neurally realized parallel distributed processing (PDP) networks. (127)

Panzarasa, Pietro and Nicholas Jennings. Collective Cognition and Emergence in Multi-Agent Systems. Sun, Ron, ed. Cognition and Multi-Agent Interaction. Cambridge: Cambridge University Press, 2006. The reality and efficacy of a collaborative realm of human intelligence and cerebration is increasingly evident and lately gaining its theoretical ground. This further, “holistic,” self-organized phase is again the result of the universal system of local communication by discrete entities.

In the last few decades, the study of collective cognition has become an increasingly interdisciplinary area of research, weaving together an array of scientific contributions from a wide variety of scholarly fields including social psychology, organization science, complex adaptive systems, social network analysis, business studies, cognitive science, computer science and philosophy of mind. The fundamental idea underpinning most of these studies is that cognition is a social phenomenon that takes place and evolves in a reality jointly constructed by agents who interact within a network of social relations. To capture this idea, several “group mind”-like constructs have been introduced that extent to the group level a range of cognitive phenomena traditionally considered as belonging to the realm of the individual agent’s mind. (401)

Papadimitriou, Christos, et al. Brain Computation by Assemblies of Neurons. Proceedings of the National Academy of Sciences. 117/14464, 2020. Veteran Columbia University, Georgia Tech, and Graz University of Technology computer scientists propose and discuss ways how the content of neural associations might be projected and traced all the way to thoughtful linguistic results.

Our expanding understanding of the brain at the level of neurons and synapses, and the level of cognitive phenomena such as language, leaves a formidable gap between these two scales. Here we introduce a computational system which promises to bridge this gap: the Assembly Calculus. It encompasses operations on assemblies of neurons, such as project, associate, and merge, which appear to be implicated in cognitive phenomena, and can be shown, analytically as well as through simulations, to be plausibly realizable at the level of neurons and synapses. We demonstrate the reach of this system by proposing a brain architecture for syntactic processing in the production of language, compatible with recent experimental results. (Significance)

Papo, David, et al. Complex Network Theory and the Brain. Philosophical Transactions of the Royal Society B. 369/20130520, 2014. With neuroscientists Javier Buldu, Stefano Boccaletti, and Edward Bullmore, an Introduction to this topical issue intended to give neural self-organization phenomena a deeper basis in statistical and computational physics. For example see An Edge-Centric Perspective on the Human Connectome: Link Communities in the Brain, by Marcel de Reus, et al, Network-Guided Pattern Formation of Neural Dynamics by Marc-Thorsten Hutt, et al, and Function Brain Networks by David Papo, et al (search). In essence, a 21st century realization that micro cerebral connectome and macro emergent “cosmome” are truly one and the same.

Here we are focused on how a more formal, quantitative analysis of complex network organization could help us to understand the brain at micro and macro scales. Specifically, we are interested in the potential value added to neuroscience by the application of contemporary complex network theory: a statistical physics understanding of graph theory, itself a much older branch of pure mathematics. The statistical physics approach aims at explaining observable macroscopic behaviour of a given system as emerging in a non-trivial way from the interactions of a vast number of microscopic units or agents. Complex network theory can be thought of as a subfield of statistical physics for structurally disordered, dynamically heterogeneous systems with non-trivial topology; and as an extension of graph theory to systems with high structural heterogeneity and inherently dynamical properties, two key properties of the vast majority of real-life systems, including brains. (2)

The abstraction of graphs from the details of the underlying data means that the same mathematical language can be used to quantify topological properties at micro and macro scales, to link the organization of anatomical and functional networks, to compare the topology of brain networks across species, and to consider the topology of brain networks in general compared with other complex systems, including non-biological networks. This in turn has encouraged a shift in perspective towards fractal, scale-invariant or indeed universal properties of brain networks that complement the traditional focus on the unique and species-specific anatomical details of their organization. (2)

Papo, David, et al. Functional Brain Networks. Philosophical Transactions of the Royal Society B. 369/20130533, 2014. In a special issue about Complex Network Theory and the Brain, Spanish and Italian systems neuroscientists contribute a statistical mechanics explanation for the myriad dynamic interconnections of neural anatomies and consequent cognizance.

At a conceptual level, the complex network approach represents a paradigm shift from a computer-like to a complex system approach to the brain. In the former approach, as is the case of computers, the brain is a collection of heterogeneous parts where function can be traced back to the computations carried out at well-defined locations and to the transport of their output from one location to another. At the system-level of investigation of standard non-invasive neuroimaging techniques, modelling typically involves a small number of units. The huge number of neurons and synapses suggests that the brain is better modelled as a complex system, capable of generating a vast repertoire of macroscopic patterns of collective behaviour with distinctive temporal, spatial or functional structures. (1-2)

The statistical mechanics approach underlying complex network theory allows conceiving of macroscopic brain function as emerging in a non-trivial way from the interactions of a vast number of microscopic neural units. The networks formed by these interactions are endowed with properties which do not depend on those of their constituent nodes: neither particular nodes, nor particular links have, at least prima facie, an identifiable role in determining network properties. These are instead essentially statistical in nature. Ultimately, observable functional abilities are but the macroscopic output of the renormalization of neural fluctuations at microscopic scales. (2) Complex network theory allows going one step further and investigating general organizing principles at all scales, reflecting the fact relevant aspects of functional brain activity, such as information storing, may be either local, or non-locally spread across widely separated units. (2)

Park, Denise and Chih-Mao Huang. Culture Wires the Brain: A Cognitive Neuroscience Perspective. Perspectives on Psychological Science. 5/4, 2010. An article in this special issue on the intersect of the cerebral and communal, wherein University of Texas and University of Illinois researchers find that the East/West social complements actually engender different neural anatomies appropriate for these self or other predilections.

The culture and cognition framework discussed thus far would predict that East Asians should be more likely to fixate on contextual information than Westerners and that Westerners should tend to fixate more on central objects. (392)

Park, Hae-Jeong and Karl Friston. Structural and Functional Brain Networks: From Connections to Cognition. Science. 342/1238411-1, 2013. What a fantastic, exemplary microcosm we peoples each have in our own heads. In a special section on “The Heavily Connected Brain,” Yonsei University College of Medicine, Seoul, and University College London, neuroscientists present, as only now possible, a consummate capsule of “the multiscale hierarchical organization of brain networks.” Extensive illustrations convey the “local, rich-club, global,” neuron node and edge link, modular organization that repeats in kind as networks stack within larger integrations. Such images could readily apply throughout evolutionary nature and society as evidences of its recurrent universality, the great discovery we are on the verge of. Dare one ask why a certain macrocosmos is trying to reconstruct and reveal itself through our earthwise human agency?

The human brain presents a puzzling and challenging paradox: Despite a fixed anatomy, characterized by its connectivity, its functional repertoire is vast, enabling action, perception, and cognition. This contrasts with organs like the heart that have a dynamic anatomy but just one function. The resolution of this paradox may reside in the brain's network architecture, which organizes local interactions to cope with diverse environmental demands—ensuring adaptability, robustness, resilience to damage, efficient message passing, and diverse functionality from a fixed structure. This review asks how recent advances in understanding brain networks elucidate the brain’s many-to-one (degenerate) function-structure relationships. In other words, how does diverse function arise from an apparently static neuronal architecture? We conclude that the emergence of dynamic functional connectivity, from static structural connections, calls for formal (computational) approaches to neuronal information processing that may resolve the dialectic between structure and function. (Abstract)

Platek, Steven, et al, eds. Evolutionary Cognitive Neuroscience. Cambridge: MIT Press, 2007. After a decade or so of academic argument, once the very idea is admitted, over how to consider temporal influences on human psychology and its neural matrix, the field of study is now proceeding with breath and depth as this volume attests. Six sections are Introduction and Overview, Neuroanatomy: Ontogeny and Phylogeny, Reproduction and Kin Selection, Spatial Cognition and Language, Self-Awareness and Social Cognition, and Theoretical, Ethical, and Social Considerations. Typical quality papers are Brain and Cognition in Evolutionary Perspective by Robin Dunbar, The Evolution of the Brain and Cognition in Cetaceans by Lori Marino, The Evolution of Language by Michael Corballis, and The Assortative Mating Theory of Autism by Simon Baron-Cohen.

These new investigations, by applying cognitive neuroscientific methods to answer questions posed from an evolutionary theoretical perspective are crafting a new understanding of how the mind and brain evolved. (xvi)

Plenz, Dietmar and Tara Thiagarajan. The Organizing Principles of Neuronal Avalanches. Trends in Neurosciences. 30/3, 2007. These spatial and temporal patterns of cerebral activity in the mammalian cortex occur in a scale-invariant way that denotes a fractal structure. A self-organized criticality is also present. In so doing, they correspond to the dynamics of neuronal cell assembly, along with similarities to such phenomena throughout nature. The work of Mark Newman and Dante Chialvo is referenced in this regard.

Power laws have been found ubiquitously in the brain in the temporal organization of channel openings, the interval distributions (1/f) of transmitter release and spike trains, as well as in the local amplitude fluctuations of the human electroencephalogram and magnetoencephalogram. (103)

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