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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Sporns, Olaf. The Non-Random Brain: Efficiency, Economy, and Complex Dynamics. Frontiers in Computational Neuroscience. 5/Article 5, 2011. What to make of all these findings? If to imagine these many research and reports as part of and achieved by a worldwide collaborative brain learning on her/his own, what philosophical implications might accrue? That is to say, how does our evolved cerebral anatomy know to take upon itself this certain form of self-organized, scale-invariant networks, as in every other realm of nature and society? As scientists increasingly note, an independent, universally applicable mathematical source seems to be at implicate creative work.

Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly non-random features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape non-random dynamic interactions that occur during spontaneous activity or in response to external stimulation. (1)

However, modern circuit mapping and neural recording studies unequivocally show that the brain is not a random network. Instead, at different levels of scale, network studies have identified a number of specific non-random structural attributes, particularly the existence of network communities interlinked by hub regions. The modular small world of brain networks simultaneously promotes their economy and efficiency, by enabling their physical realization at low cost of wiring volume and metabolic energy, while also allowing efficient information flow. Non-random structure leads to the emergence of complex dynamics, generating a diverse repertoire of brain states that are differentially engaged during ongoing neural activity as well as in response to stimulation and task demands. (11)

Sporns, Olaf and Christopher Honey. Small Worlds Inside Big Brains. Proceedings of the National Academy of Sciences. 103/19219, 2006. This note comments on a research report by Bassett, Danielle, et al. Adaptive Reconfiguration of Fractal Small-World Human Brain Functional Networks. in the same issue which finds and verifies the presence of a consistent nest of structure and system.

Perhaps the most remarkable finding of the study by Bassett, et al is the relative invariance of the network topology across all physiologically relevant frequency bands, forming a self-similar or fractal architecture. (19219) Thus it appears that brain networks preserve global topological characteristics (continually maintaining the balance of efficient local and global processing) while flexibly adapting the specifics of the topology to satisfy changing task demands. (19220)

Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters. (Bassett, et al, 19518) We have shown that there is a scale-invariant or fractal organization of large scale brain functional networks in the resting state, which consistently demonstrate small-world properties in the scaling regime 2-37.5 Hz. (Bassett, et al, 19522)

Sporns, Olaf, et al. Organization, Development and Function of Complex Brain Networks. Trends in Cognitive Systems. 8/9, 2004. The same scale-free principles found in every natural and social realm are equally present in the human brain. Cortical systems in their intricate connectivity exhibit a robust small world, scale-free architecture.

We suggest that network analysis offers new fundamental insights into global integrative aspects of brain function, including the origin of flexible and coherent cognitive state within the neural architecture. (418)

Stam, Cees and Elizabeth van Straaten. The Organization of Physiological Brain Networks. Clinical Neurophysiology. 123/1067, 2012. VU University Medical Center, Amsterdam, neurophysicians describe and attest well to how much of a natural microcosm are actually we, especially in the anatomy and cogitation of our cerebral endowment.

There is an urgent need to understand the brain as a complex structural and functional network. Interest in brain network studies has increased strongly with the advent of modern network theory and increasingly powerful investigative techniques such as "high-density EEG", MEG, functional and structural MRI. Modern network studies of the brain have demonstrated that healthy brains self-organize towards so-called "small-world networks" characterized by a combination of dense local connectivity and critical long-distance connections. In addition, normal brain networks display hierarchical modularity, and a connectivity backbone that consists of interconnected hub nodes. This complex architecture is believed to arise under genetic control and to underlie cognition and intelligence. (Abstract)

Stephen, Damian, et al. The Dynamics of Insight: Mathematical Discovery as a Phase Transition. Memory & Cognition. 37/8, 2011. With coauthors Rebecca Boncoddo, James Magnuson, and James Dixon, University of Connecticut research psychologists add another take upon the scientific witness of a universally self-organizing materiality, which can be see arising from the “physical” cosmos to human cognitive faculties. And might our whole earth learn as its sequential trajectory now ascends to a global sphere? See also Stephen’s web publications page, and Dixon, et al, below for on-going work.

In recent work in cognitive science, it has been proposed that cognition is a self-organizing, dynamical system. However, capturing the real-time dynamics of cognition has been a formidable challenge. Furthermore, it has been unclear whether dynamics could effectively address the emergence of abstract concepts (e.g., language, mathematics). Here, we provide evidence that a quintessentially cognitive phenomenon — the spontaneous discovery of a mathematical relation — emerges through self-organization. (Abstract, 1132)

Attempts to understand how such networks change during learning has revealed that many of these models operate under the principles of self-organization from nonlinear dynamics. The same higher order relations that govern self-organization in a wide variety of other domains, such as fluids, lasers, and ferromagnets, are exhibited by a class of connectionist models that learn via Hebbian and SOM algorithms. (1134) The present study suggests that the reach of self-organization extends to the spontaneous formation of new structures at the conceptual level. Even quite abstract concepts, such as mathematical relation, emerge according to the principles of self-organization. (1144)

Stoop, Ralph, et al. Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains. Physical Review Letters. 110/108105, 2013. By way of sophisticated theory and experiment, Swiss neurophysicists add insight to the nonlinear topologies and thoughtful dynamics that grace cerebral anatomy and activity. To wit, it’s not dots, or neuronal columns, that matter as much as the relational connections between them. And this surmise seems to equally apply throughout nature and society from genomes to communities. This clever work was highlighted online by the journal sponsor, the American Physical Society APS, an excerpt of its notice is below.

We study to what extent cortical columns with their particular wiring boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of enhancement. It is on a mesoscopic—intercolumnar—scale that the existence of columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks. (Article Abstract)

Synopsis: A Double Power Law Powers Brain. The extraordinary complexity of the brain makes it hard to identify its underlying organizational principles. Ralph Stoop, at the University of Basel, Switzerland, and colleagues used computational models of neural networks to deduce that the details of the organization within individual columns are not very important. Instead, what counts is how different columns are interconnected. To study the importance of the “wiring” configuration within columns, the team arranged mathematical models of neurons into networks and compared configurations with different connectivities. Interestingly, the ability of these simulated columns to carry out a computational task, such as the classification of Arabic digits, did not significantly improve when the connection strengths or the layered arrangement were chosen to mimic those often seen in biological columns.

In contrast, the researchers found that the connections between columns in a side-by-side sheet made a big difference to the speed with which information propagated laterally to coordinate activity across the simulated cortex. The authors compared networks with different spatial distributions of connections between simplified columns. For example, in “scale-free” networks—including many real-world networks—the number of connections decreases with their length as a single power law, so there are few relatively long links. But Stoop and his colleagues found that, for the same total length of “wires,” signals spread more quickly in a network described by two power laws. This distribution, which was suggested by microscopy investigations in lab animals, includes a larger number of very long connections that help information to propagate quickly between distant columns. (Don Monroe, APS)

Stuart, Susan and Gordana Dodig Crnkovic, eds. Computation, Information, Cognition: The Nexus and the Liminal. Newcastle: Cambridge Scholars Publishing, 2007. Some 25 chapters engage these technical domains of neural net nodes and links and their biosemiotic activities. (Web definitions of iminal: ambiguity, openness, and indeterminacy, poised between two states.) Typical papers are Meaning and Self-Organization in Cognitive Science by Arturo Carsetti and The Informational Architectures of Biological Complexity by Pedro Marijuan and Raquel del Moral. Heavy slogging which may yet imply that our reality in inherently textual in kind, if we could only learn together to translate and read its salutary message.

Suddendorf, Thomas, et al. Prospection and Natural Selection. Current Opinion in Behavioral Science. 24/26, 2018. University of Queensland and Federation University, Australia psychologists contribute to this growing movement in cognitive science (Friston) which views neural faculties as most oriented to deal with future experiences and their response. See Suddendorf’s publication page for many articles in about this significant turn.

Prospection refers to thinking about the future, a capacity that has become the subject of increasing research in recent years. Here we first distinguish basic prospection, such as associative learning, from more complex prospection commonly observed in humans, such as episodic foresight, the ability to imagine diverse future situations and organize current actions accordingly. We review recent studies on complex prospection in various contexts, such as decision-making, planning, deliberate practice, information gathering, and social coordination. Prospection appears to play many important roles in human survival and reproduction. (Abstract)

Summerfield, Christopher and Kevin Miller.. Computational and Systems Neuroscience: The next 20 years. PLoS Biology. September, 2023. Google Deep Mind, London and University College London review the past two 21st century decades as a forming a global endeavor was formed which then proceeded to realize and develop a complex neural net systems perspective. At our present worldwise confluence, future efforts need carefully manage and assimilate vast AI capacities so they serve a comprehensive and palliative knowledge.

Over the past 20 years, neuroscience has been propelled forward by theory-driven experimentation. We consider the future outlook for the field in the age of big neural data and powerful artificial intelligence models.

Sun, Ron, ed. Cognition and Multi-Agent Interaction. Cambridge: Cambridge University Press, 2006. A technical volume on the latest work about the validity and explanation of cerebral activities in human social assemblies. For example, see Panzarasa and Jennings below. At what point then do such evolving communities begin to achieve their “own” organic identity and integral knowledge? That such a spherical cognitive compression, if we might avail ourselves, is taking place on a global scale is the basis of this sourcebook website.

Swanson, Larry. Brain Architecture. Oxford: Oxford University Press, 2003. A proficient introductory survey to the evolution and basic principles of sensory and cerebral systems from neurons to cognition.

Tagliazucchi, Enzo and Dante Chialvo. The Collective Brain is Critical. arXiv:1103.2070. University of Buenos Aires and UCLA neuroscientists (search Chialvo) strongly state that as everywhere else, neural anatomy and cogitation is also critically poised between chaos and order, (as we well know) which is actually a smart and effective place to be. See Gyorgy Buzsaki for a companion take. What kind of universe then seems inherently and persistently driven to its own intelligence and self-recognition?

In the nineties, the fundamental concepts behind the physics of complex systems, motivated us to work on ideas that now seem almost obvious: 1) the mind is a collective property emerging from the interaction of billions of agents; 2) animate behavior (human or otherwise) is inherently complex; 3) complexity and criticality are inseparable concepts. These points were not chosen arbitrarily, but derived, as discussed at length here, from considering the dynamics of systems near the critical point of a order-disorder phase transition. (1)

Emergence refers to the observation of dynamics that is not expected from the systems equations of motion and, almost by (circular) definition, is exhibited by complex systems. As discussed at length elsewhere [3, 15, 17, 19, 37, 47, 56], three features are present in complex systems: (I) they are large conglomerate of interacting agents, (II) each agent own dynamics exhibits some degree of nonlinearity and (III) energy enters the system. [60] These three components are necessary for a system to be able to exhibit, at some point, emergent behavior. (1)

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