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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Park, Hae-Jeong and Karl Friston. Structural and Functional Brain Networks: From Connections to Cognition. Science. 342/1238411-1, 2013. What a fantastic, exemplary microcosm we peoples each have in our own heads. In a special section on “The Heavily Connected Brain,” Yonsei University College of Medicine, Seoul, and University College London, neuroscientists present, as only now possible, a consummate capsule of “the multiscale hierarchical organization of brain networks.” Extensive illustrations convey the “local, rich-club, global,” neuron node and edge link, modular organization that repeats in kind as networks stack within larger integrations. Such images could readily apply throughout evolutionary nature and society as evidences of its recurrent universality, the great discovery we are on the verge of. Dare one ask why a certain macrocosmos is trying to reconstruct and reveal itself through our earthwise human agency?

The human brain presents a puzzling and challenging paradox: Despite a fixed anatomy, characterized by its connectivity, its functional repertoire is vast, enabling action, perception, and cognition. This contrasts with organs like the heart that have a dynamic anatomy but just one function. The resolution of this paradox may reside in the brain's network architecture, which organizes local interactions to cope with diverse environmental demands—ensuring adaptability, robustness, resilience to damage, efficient message passing, and diverse functionality from a fixed structure. This review asks how recent advances in understanding brain networks elucidate the brain’s many-to-one (degenerate) function-structure relationships. In other words, how does diverse function arise from an apparently static neuronal architecture? We conclude that the emergence of dynamic functional connectivity, from static structural connections, calls for formal (computational) approaches to neuronal information processing that may resolve the dialectic between structure and function. (Abstract)

Platek, Steven, et al, eds. Evolutionary Cognitive Neuroscience. Cambridge: MIT Press, 2007. After a decade or so of academic argument, once the very idea is admitted, over how to consider temporal influences on human psychology and its neural matrix, the field of study is now proceeding with breath and depth as this volume attests. Six sections are Introduction and Overview, Neuroanatomy: Ontogeny and Phylogeny, Reproduction and Kin Selection, Spatial Cognition and Language, Self-Awareness and Social Cognition, and Theoretical, Ethical, and Social Considerations. Typical quality papers are Brain and Cognition in Evolutionary Perspective by Robin Dunbar, The Evolution of the Brain and Cognition in Cetaceans by Lori Marino, The Evolution of Language by Michael Corballis, and The Assortative Mating Theory of Autism by Simon Baron-Cohen.

These new investigations, by applying cognitive neuroscientific methods to answer questions posed from an evolutionary theoretical perspective are crafting a new understanding of how the mind and brain evolved. (xvi)

Plenz, Dietmar and Tara Thiagarajan. The Organizing Principles of Neuronal Avalanches. Trends in Neurosciences. 30/3, 2007. These spatial and temporal patterns of cerebral activity in the mammalian cortex occur in a scale-invariant way that denotes a fractal structure. A self-organized criticality is also present. In so doing, they correspond to the dynamics of neuronal cell assembly, along with similarities to such phenomena throughout nature. The work of Mark Newman and Dante Chialvo is referenced in this regard.

Power laws have been found ubiquitously in the brain in the temporal organization of channel openings, the interval distributions (1/f) of transmitter release and spike trains, as well as in the local amplitude fluctuations of the human electroencephalogram and magnetoencephalogram. (103)

Plenz, Dietmar, et al. Criticality in Neural Systems. Weinheim: Wiley-VCH, 2014. This nascent view of cerebral self-organization and cogitation has matured via theoretical and experimental studies over the past decade to now merit a book-length treatment. Chapters by leading proponents include The Dynamic Brain in Action by Scott Kelso, Critical Brain dynamics by Dante Chialvo, and Complex Networks: From Social Crises to Neuronal Avalanches by Bruce West, et al, twenty-four all told.

Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. (Chialvo)

Ponce-Alvarez, Adrian, et al.. Critical Scaling of Whole-Brain Resting-State Dynamics. Communications Biology. June, 2023. Into mid 2023, Universitat Pompeu, Spain and Oxford University system psychologists including Gustavo Deco post another integrative synthesis with an array of self-similar affinities and features. A common presence of many local interactive entities is seen to give rise to a nested sequence of group-like collectivities. As this convergent synthesis proceeds, an opening paragraph (below) describes its iconic cerebral occasion. A further dimension is then added by relation this behavior with a physical substrate by way of phase transition and renormalization theories.

Scale invariance is a characteristic of neural activity. How this property emerges from neural interactions remains to be explained. Here, we studied the relation between brain dynamics and structural connectivity by way of human resting-state fMRI signals, together with diffusion MRI (dMRI) connectivity and its approximation as exponentially decaying function of the distance between brain regions. We analyzed the fMRI dynamics using functional connectivity and a recently proposed renormalization group (PRG) method that tracks the change of collective activity after successive coarse-graining at different scales. We found that brain dynamics display power-law correlations as a function of PRG coarse-graining based on relative connectivity. (Abstract excerpt)

Growing evidence indicates that large-scale spontaneous brain activity is an emergent phenomenon continuously generating patterned activity at multiple spatiotemporal scales. As a hallmark of resting-state, whole-brain activity is scale invariant, which in physical systems is observed at critical points. Thus, the observation of power-law statistics in neural activity supports the idea that neural activity operates close to a phase transition. Such critical neural systems then maximize information transmission, storage, and processing. This is achieved through the Renormalization Group (RG) procedure. (1)

Pospelov, Nikita, et al. Spectral Peculiarity and Criticality of a Human Connectome. Physics of Life Reviews. Online June 16, 2019. Six Russian neurotheorists based at Lomonosov Moscow State University describe novel techniques and insights which adds more evidence that our hyperactive brains are truly situated at an optimum critically poised state.

We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. We found that the spectral density of adjacency matrices of human connectome has maximal deviation from randomized networks, compared to other organisms. We discovered that for macaque and human connectomes the conservation of local clusterization is crucial, while for primitive organisms the conservation of averaged clusterization is sufficient. We found that the level spacing distribution of the spectrum of human connectome Laplacian matrix corresponds to the critical regime. This observation provides strong support for debated statement of the brain criticality. (Abstract)

Pribram, Karl and Joseph King, eds. Learning as Self-Organization. Mahwah, NJ: Erlbaum, 1996. A collection of innovative papers from both scientific and traditional (Asian) perspectives on the revolution in neuroscience due to nonlinear theories.

Priesemann, Viola, et al. Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans. PLoS Computational Biology. 9/3, 2013. Priesemann, MPI Brain Research, with Mario Valderrama, University of Los Andes, Michael Wibral, Goethe University, and Michel Le Van Quyen, CRICM, Paris, achieve a robust affirmation that cerebral faculties employ an optimal state of critical, self-organized poise. Search Wibral, et al for a 2014 paper with Priesemann for further frontiers of computational, SOC neural net research.

Neuronal activity differs between wakefulness and sleep states. In contrast, an attractor state, called self-organized critical (SOC), was proposed to govern brain dynamics because it allows for optimal information coding. But is the human brain SOC for each vigilance state despite the variations in neuronal dynamics? We characterized neuronal avalanches – spatiotemporal waves of enhanced activity - from dense intracranial depth recordings in humans. We showed that avalanche distributions closely follow a power law – the hallmark feature of SOC - for each vigilance state. However, avalanches clearly differ with vigilance states: slow wave sleep (SWS) shows large avalanches, wakefulness intermediate, and rapid eye movement (REM) sleep small ones. Our SOC model, together with the data, suggested first that the differences are mediated by global but tiny changes in synaptic strength, and second, that the changes with vigilance states reflect small deviations from criticality to the subcritical regime, implying that the human brain does not operate at criticality proper but close to SOC. Independent of criticality, the analysis confirms that SWS shows increased correlations between cortical areas, and reveals that REM sleep shows more fragmented cortical dynamics. (Abstract)

Psujek, Sean, et al. Connection and Coordination: The Interplay Between Architecture and Dynamics in Evolved Model Pattern Generators. Neural Computation. 18/3, 2006. The same complex network geometries that occur throughout nature are present in neural systems, in this case with regard to simulation of a walking task via neuron excitabilities and connections.

From molecules to cells to animals to ecosystems, biological systems are typically composed of large numbers of heterogeneous nonlinear dynamical elements densely interconnected in specific networks. (729)

Pu, Jiangbo, et al. Developing Neuronal Networks: Self-Organized Criticality Predicts the Future. Nature Scientific Reports. 3/1081, 2013. Britton Chance Center for Biomedical Photonics, Wuhan National Lab for Optoelectronics, Huazhong University of Science and Technology, systems neuroscientists again discern and confirm how nature’s universal creativity similarly graces our cerebral anatomy, physiology, and consequent thought patterns and processes. And since these phenomena appear to have an apparently independent, dynamic sequence, the forward course of self-organizing cerebration augurs toward potential future states.

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitromaturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and ‘‘aging’’ process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future. (Abstract)

Our data suggest that a self-organized criticality mechanism with long range interactions hereby plays a potential role in the emergence of metastable activity states in an evolving network. In temporally evolving networks, the coexistence of self-organized criticality and metastable state transition showed in our results provides an unprecedented experimental evidence for the hypothesis that critical networks should simultaneously exhibit criticality and metastability. Understanding the self-organized nature of developing networks may hold the key to elucidating the network-level mechanisms of brain development. Based on our result, it may be possible to predict how the network will evolve by examining the criticality in early stages. It will open a door to the investigation of age-related neuronal dysfunction, and ultimately to the forecasting of developmental dynamics of the brain. (5)

Raghavan, Guruprasad and Matt Thomson. Neural Networks Grown and Self-Organized by Noise. arXiv:1906.01039. We cite this entry by Caltech bioengineers for the way it implies an internal drive and direction that is an intelligence gaining, self-learning, quickening genesis. As these observation grow in breadth and veracity, they suggest a natural presence that seems to require at some far point the achieve its own witness and affirmation.

Living neural networks in the brain perform an array of computational and information processing tasks including sensory input processing, storing and retrieving memory, decision making, and more globally, generate the general phenomena of “intelligence”. In addition to their information processing feats, brains are unique because they are computational devices that actually self-organize their intelligence. In fact brains ultimately grow from single cells during development. Engineering has yet to construct artificial computational systems that can self-organize their intelligence. In this paper, inspired by neural development, we ask how artificial computational devices might build themselves without human intervention. (1)

Ramus, Franck. Genes, Brain, and Cognition. Cognition. 101/2, 2006. An introduction to a special issue on the interdisciplinary juncture and cross-fertilization of these often removed domains, which are seen to be at mid-points in both directions.

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