VII. WumanKinder: An EarthSphere Transition in Individuality
2. Systems Neuroscience: Multiplex Networks and Critical Function
Kelso, Scott, et al. Outline of a General Theory of Behavior and Brain Coordination. Neural Networks. 37/1, 2013. Kelso, with coauthors Guillaume Dumas and Emmanuelle Tognoli, are Florida Atlantic University, Center for Complex Systems & Brain Sciences, researchers who explain how human brains are so dynamically self-composed, poised and intelligent. As neuroscientists Danielle Bassett, Stephen Grossberg, and others also aver, a prime cerebral quality is a reciprocal interplay between whole brain or area module coherences and nested scales of semi-autonomous neural nets. Several themes might then be gleaned. At the outset, a “neural choreography” motif is cited to express both dancers and score. A multi-level model is then deployed from single neurons and local field potentials to disparate regions, global integrations, and even inter-personal synchronies (search Dumas). Again this balance repeats at every range – independent in the small and a necessary lucidity in the large. A “Metastable Brain” is thus conceived, soon to be a 2014 book by the authors. Scott Kelso’s 2006 The Complementary Nature, with Dennis Engstrom (whom emailed me to say my review was one of the best appreciations of their work) offers a luminous survey. See also “Enlarging the Scope: Grasping Brain Complexity” by Tognoli and Kelso at arXiv: 1310.7277 (October 2013). So akin to Scott Gilbert’s symbiotic organisms and everywhere else, nature’s “me + We = US” mutual viability holds once more.
Kiebel, Stefan, et al. A Hierarchy of Time-Scales and the Brain. PLoS Computational Biology. 4/11, 2008. With Jean Daunizeau and Karl Friston, Wellcome Trust Centre, University College London, neuroscientists quantify the presence of direct structural parallels between us “ontogenetic adaptive agents,” and a person’s dynamic, scalar (phylogenetic) environment. Compare, for example, with Altamura, et al (2012) above for evidences that we ourselves, our very cerebral, cognitive faculty, again seems a human epitome of the genesis universe.
In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. (Abstract) We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. (Abstract)
Kirchhoff, Michael. Predictive Brains and Embodied, Enactive Cognition: An Introduction to the Special Issue. Synthese. 195/6, 2018. An editorial for a Special Issue on Predictive Brains and Embodied, Enactive Cognition by the University of Wollongong, Australia philosopher which reviews and assimilates these dynamic neuroscientific, thought-provoking vistas. See also his own entry Autopoiesis, Free Energy, and the Life-Mind Continuity Thesis which continues to join these schools which are traced back to the work of Francisco Varela, see the second Abstract.
All the papers in this special issue intersect work on predictive processing models in the neurosciences and embodied, enactive perspectives on mind. All contributions deal with questions of whether and how key assumptions of the predictive brain hypothesis can be reconciled with approaches to cognition that take embodiment and enaction as playing a central and constitutive role in our cognitive lives. While there is broad consensus that bodily and worldly aspects matter to cognition, predictive processing is often understood in epistemic, inferential and representational terms. Rather than stressing how these accounts differ, others such as Andy Clark (University of Edinburgh) emphasize what they have in common, focusing on how predictive processing models provide “the perfect neuro-computational partner for work on the embodied mind.” Our aim is to nudge this particular area of research forward by examining how to combine the best of these frameworks in a joint pursuit. (Intro Abstract excerpt)
Knyazeva, Helena. Nonlinear Cobweb of Cognition. Foundations of Science. 14/3, 2009. The Evolutionary Epistemology program director at the Institute of Philosophy of the Russian Academy of Sciences here proposes to recast cerebral activities, both for persons and contextual nature, in terms of complex system phenomena. As a result, a constructivist, enactive emergence appears as a “coming to be” self-realization for both individuals and encompassing cosmos, each engaged in a vast learning process.
Cognition is dynamical and under construction in the processes of self-organization. In other words, cognitive systems are dynamical and self-organizing ones. The functioning of cognitive systems is similar in principle to the function of natural systems which undergo our cognition, i.e., of objects of the surrounding world, the essence of there processes is analogous. Therefore, in the frames of the embodied cognition approach called also the dynamical approach in cognitive science, the latest developments in the fields of nonlinear dynamics, the theory of complex adaptive systems, the theory of self-organized criticality and synergetics are widely and fruitfully used. (171)
Koch, Christoph and Gilles Laurent. Complexity and the Nervous System. Science. 284/96, 1999. A survey article on the explanatory value of dynamical theories in neuroscience.
While everyone agrees that brains constitute the very embodiment of complex adaptive systems and that Albert Einstein’s brain was more complex than that of a housefly, nervous system complexity remains hard to define.…Any realistic notion of brain complexity must incorporate, first the highly nonlinear, nonstationary and adaptive nature of the neuronal elements themselves and, second, their nonhomogeneous and massive parallel patterns of interconnections whose ‘weights’ can wax and wane across multiple time scales in behaviorally significant ways. (98)
Kozma, Robert and Walter Freeman. Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems. Frontiers of Systems Neuroscience. Online February, 2017. The University of Memphis mathematician is here joined by the late (1927-2016) UC Berkeley pioneer theorist of our cerebral activity by the grace of nonlinear complexities. We cite for its content, and as an exemplar of nature’s tendency to reside in a critically poised optimum state between too much chaos or order. A middle balance of these reciprocal complements, as perennial wisdom teaches, is ever best. A worst out-of-kilter situation might then be American politics where they are locked in mutual destruction.
Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We employ random graph theory and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli. (Abstract excerpts)
Kwisthout, Johna, et al. Special Issue on Perspectives on Human Probabilistic Inference and the Bayesian Brain. Brain and Cognition. 112/1, 2017. An issue editorial for a collection of papers broadly about the predictive brain theory of Karl Friston and many colleagues. See for example, The Infotropic Machine, A Social Bayesian Brain, and Explanatory Pluralism.
Lake, Blue, et al. Neuronal Subtypes and Diversity Revealed by Single-Nucleus RNA Sequencing of the Human Brain. Science. 352/1586, 2016. We cite this contribution cited by the journal as Neurogenomics by a 21 member team at UC San Diego and the Scripps Institute as a good example of how genomic and cerebral (neuromics?) studies have come to employ and share similar techniques and vernaculars, as if the same phenomena. See also Canonical Genetic Signatures of the Adult Human Brain and Transcriptional Architecture of the Human Brain, both in Nature Neuroscience (18/12, 2015). And at this point, we would like to make notice that other domains such as linguistics, cosmic webs, quantum systems, are also adopting sequence techniques, as common genetic code becomes evident everywhere.
The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain. (Abstract)
Le Van Quyen, Michel. The Brainweb of Cross-scale Interactions. New Ideas in Psychology. 29/2, 2011. In similar, independent work to Herculano-Houzel and colleagues in Brazil, a Université Pierre et Marie Curie, Paris, neuroscientist describes a multi-level cerebral cognition from neuron and synapse to whole brain nets that exhibits both upward and downward causation, from which then arises conscious awareness. Upon reflection, one gets impression in each of these cases, of a spatial and temporal microcosm in our own heads of a macrocosmic genesis just ascending to conscious individuation through the human phenomenon.
From neuron to behaviour, the nervous system operates on many levels of organization, each with its own scales of time and space. Very large sets of data can now be obtained from these multiple levels by the explosive growth of new physiological recording techniques and functional neuroimaging. Among the most difficult tasks are those of conceiving and describing the exchanges between levels, seeing that the scales of time and distance are braided together in a complex web of interactions, and that causal inference is far more ambiguous between than within levels. In this paper, I propose that a generic description of these multi-level interactions can be based on the temporal coordination of neuronal oscillations that operate at multiple frequencies and on different spatial scales. (Abstract)
Levina, Anna, et al. Dynamical Synapses Causing Self-Organized Criticality in Neural Networks. Nature Physics. 3/857, 2007. With co-authors Michael Herrmann and Theo Geisel, a progress report on realizations that our brains are in fact, a revelatory epitome of a quickening, awakening, individuating, genesis cosmos.
Self-organized criticality is one of the key concepts to describe the emergence of complexity in natural systems. The concept asserts that a system self-organizes into a critical state where system observables are distributed according to a power law. Prominent examples of self-organized critical dynamics include piling of granular media, plate tectonics and stick–slip motion. Critical behaviour has been shown to bring about optimal computational capabilities, optimal transmission, storage of information and sensitivity to sensory stimuli. In neuronal systems, the existence of critical avalanches was predicted and later observed experimentally. Here, we demonstrate analytically and numerically that by assuming (biologically more realistic) dynamical synapses in a spiking neural network, the neuronal avalanches turn from an exceptional phenomenon into a typical and robust self-organized critical behaviour, if the total resources of neurotransmitter are sufficiently large.
Lynn, Christopher, et al. Human Information Processing in Complex Networks. arXiv:1906.00926. University of Pennsylvania neuroengineers including Danielle Bassett contribute to the network revolution by showing how this connectomic feature serves our cognitive performance. See also A Mathematical Theory of Semantic Development in Deep Neural Networks by Andrew Saxe, et al (herein) for a similar concurrent study.
Humans communicate using systems of interconnected stimuli or concepts from language and music to literature and science yet it remains unclear how the structure of these networks supports this process. Here we demonstrate that this perceived information depends on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (high entropy) and do so efficiently (low divergence from expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules. These results suggest that many real networks are constrained by the pressures of information transmission, and that they select for specific structural features. (Abstract excerpt)
MacCormac, Earl and Maxim Stamenov, eds. Fractals of Brain, Fractals of Mind. Philadelphia: John Benjamin Publishing, 1996. How the sciences of complexity can reveal an intrinsic self-organization of brain development and behavior which takes on a fractal-like structure across many different spatial scales.