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

2. Systems Neuroscience: Multiplex Networks and Critical Function

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.

Lofti, Nastaran, et al. Statistical Complexity is Maximized Close to Criticality in Cortical Dynamics. arXiv:2010.040123. Nine Brazilian neuroscientists contribute to a growing notice that cerebral activity tends and prefers to reside in this optimum balance. See also Quasicritical Brain Dynamics by Leandro Fosque, et al at 2010.02938 and Testing the Critical Brain Hypothesis using a Phenomenological Renormalization Group by Giorgio Nicoletti, et al at 2001.04353 for further work.

Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity to desynchronized and disordered regimes. It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. We show that statistical complexity is maximized close to criticality for cortical spiking data, as well as for a network model of excitable elements at a critical point of a non-equilibrium phase transition. (Abstract excerpt)

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.

Majhi, Soumen, et al. Chimera States in Neuronal Networks. Physics of Life Reviews. September, 2018. As complex network studies proceed apace, Indian Statistical Institute, Kolkata, and University of Maribor, Slovenia (Matjaz Perc) join a growing notice that brains seem to seek and reside at an optimum coexistence between a more or less orderly, conserve/create condition.

Neuronal networks, similar to many other complex systems, self-organize into fascinating emergent states that are not only visually compelling, but also vital for the proper functioning of the brain. Recent research has shown that the coexistence of coherent and incoherent states, known as chimeras, is particularly important characteristic for neuronal systems. The emergence of this unique collective behavior is due to diverse factors that characterize neuronal dynamics and the functioning of the brain in general, including neural bumps and unihemispheric slow-wave sleep in some aquatic mammals. (Abstract excerpt)

Mandelblit, Nili and Oron Zachar. The Notion of Dynamic Unit: Conceptual Developments in Cognitive Science. Cognitive Science. 22/2, 1998. The article describes a model akin to complex adaptive systems with applicability at every phase from physical substrates to neural processes, linguistics, and a collective social cognition.

We suggest a common ground for alternative proposals in different domains of cognitive science which have previously seemed to have little in common. Our framework suggests a definition of unity which is based not on inherent properties of the elements constituting the unit, but rather on dynamic patterns of correlation across the elements. (229)

Markman, Arthur and Eric Dietrich. Extending the Classical View of Representation. Trends in Cognitive Sciences. 4/12, 2000. An attempt to sort through several conflicting approaches to how the brain remembers and responds by considering theories of perceptual symbol systems, situated action, embodied cognition and dynamical systems.

Martone, Maryann, et al. e-Neuroscience: Challenges and Triumphs in Integrating Distributed Data from Molecules to Brains. Nature Neuroscience. 7/5, 2004. From a complete issue on the subject, a review of how a collaborative field of neuroinformatics, similar to bioinformatics, is coming together to handle and integrate the vast amount of brain imaging and other neurological data pouring forth from laboratories worldwide.

McClelland, James, et al. Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition. Trends in Cognitive Sciences. 14/8, 2010. Seven neuroscientists from Stanford, Princeton, University of California, Carnegie Mellon, University of Wisconsin and Indiana University (Linda Smith) contribute to the on-going reinvention of all things cerebral and clever in terms of nature’s complex systems. Of which the lead author and David Rumelhart were pioneers with 1980s parallel processing. See also McClelland’s “Emergence in Cognitive Science” in Topics in Cognitive Science (4/2, 2010) for an extensive acclaim of this property. In the same journal for May 2011, Danielle Bassett and Michael Gazzaniga offer “Understanding Complexity in the Human Brain” as a similar articulation.

Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. (348)

McNally, Luke, et al. Cooperation and the Evolution of Intelligence. Proceedings of the Royal Society B. Online April, 2012. With Sam Brown and Andrew Jackson, Trinity College Dublin, and University of Edinburgh, zoologists provide more credence for the “social brain” model, to wit if entities could ever stop fighting and actually help each other, it quite fosters learning activities, good for individuals and tribes to survive and thrive.

The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the ‘social intelligence hypothesis’), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa. (Abstract)

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