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

2. Systems Neuroscience: Multiplex Networks and Critical Function

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.

Li, Mike, et al. Transitions in Information Processing Dynamics at the Whole-Brain Network Level are Driven by Alterations in Neural Gain. PLoS Computational Biology. October, 2019. University of Sydney and Stanford University neuroscientists once more well quantify how cerebral processes perform at their best by a reciprocity of relative separations and whole assemblies.

Dynamic brain complexity can adapt its functional network structure between integrated and segregated states in response to different cognitive tasks. In this study, we show that the dynamics of the subcritical (segregated) regime are involved with information storage, while the supercritical (integrated) phase is more associated with information transfer. Operating near to the critical state by modulating neural gain parameters thus appears to provide computational advantages and flexibility in information processing. (Abstract excerpt)

Liang, Junhao and Changsong Zhou. Criticality Enhances the Multilevel Reliability of Stimulus Responses in Cortical Neural Networks. PLOS Computational Biology.. January, 2022. We cite this entry by Beijing-Hong Kong-Singapore, Joint Centre for Nonlinear and Complex Systems researchers for one more notice of nature’s prime preference to seek and reside at an optimum balance between two opposite but reciprocal states. Our especial is a tendency toward Chimera-like active or static modes.

The complexity of dynamical brain activity ranges from neuronal spiking and neural avalanches to oscillatory potentials of local circuits across many states. Such multilevel variables are functionally and behaviorally relevant. To better clarify, we study the stimulus–response of neural circuits. Our model assumes excitation–inhibition (E–I) interactions and synaptic couplings with a critical sub-region. We further analyze the nonlinear dynamical principles using a novel and a broadly applicable mean-field theory. (Summary excerpt)

Lindsay, Grace. Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.. London: Bloomsbury Sigma, 2021. An Assistant Professor of Psychology and Data Science at New York University begins her book with a review of mathematic and physical principles so they can be applied to cognitive functions such as memories, vision, decision making, excitation/inhibition. With this currency in place, some Grand Unified Theories are surveyed such as Karl Friston’s free energy, Jeff Hawkins’ Thousand Brains project, and Giulio Tononi’s integrated information model. And one wonders whomever is this late planetary faculty as it proceeds to learns on its own. What manner of multiuniverse needs to form a midway self-representation, realization and participatory affirmation?

In Models of the Mind, computational neuroscientist Grace Lindsay explains how mathematical models have allowed researchers to understand and describe many of the brain's processes such as decision-making, sensory processing, stored memory, and more. Each chapter focuses on mathematical tools that have been applied from the individual neuron to their many interactions, whole brain areas and the consequent behaviours. In addition, Grace examines the history of the field from the eighteenth century and to the large models of neural networks that form the basis of modern artificial intelligence.

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. Broken Detailed Balance and Entropy Production in the Human Brain. PNAS. 118/47, 2021. We cite this technical exercise by CCNY, Princeton, and University of Pennsylvania researchers including Danieile Bassett because it is able to connect our cerebral functions all the way to a complex physical basis. With this in place, a wider creative presence is noted across natural and societal phenomena.

To perform biological functions, living systems must break detailed balance by consuming energy and producing entropy. At microscopic scales, broken detailed balance enables a suite of molecular and cellular functions, including computations, kinetic proofreading, sensing, adaptation, and transportation. But do macroscopic violations of detailed balance enable higher-order biological functions, such as cognition and movement? To answer this question, we adapt tools from nonequilibrium statistical mechanics to quantify broken detailed balance in complex living systems. (Significance)

More generally, we remark that the methods not only apply to the brain, but can be broadly used to investigate broken detailed balance in other living systems, including emergent behavior in human and animal populations, correlated patterns of neuronal firing, and collective activity in molecular and cellular networks. (5)

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)

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