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

2. Systems Neuroscience: Multiplex Networks and Critical Function

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)

Tetzlaff, Christian, et al. Self-Organized Criticality in Developing Neuronal Networks. PLoS One. 6/12, 2010. By way of a companion approach to Paul Expert, et al above, here University of Gottingen, Freiberg, and Amsterdam computational neuroscientists find a similar “…interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.”

Thompson, Evan and Francisco Varela. Radical Embodiment: Neural Dynamics and Consciousness. Cognitive Sciences. 5/10, 2001. One of Francisco Varela’s last contributions which finds sentient awareness to be rooted in an “enactive” brain-body-world interplay rather than confined to purely neuronal events.

Tognoli, Emmanuelle and Scott Kelso. The Metastable Brain. Neuron. 81/1, 2014. The Florida Atlantic University, Human Brain and Behavior Laboratory, neurosciencists provide a summary update of their insights into our dynamic cerebral reciprocities of autonomous neurons and modular syntheses. A 2014 book by this title is in the works, search also for Kelso, et al, 2009 and 2013.

Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain’s dynamic cognitive, behavioral, and social functions. (Abstract)

[Prev Pages]   Previous   | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17  Next