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

2. Systems Neuroscience: Multiplex Networks and Critical Function

He, Biyu, et al. Scale-free Dynamics and Critical Phenomena in Cortical Activity. Frontiers in Fractal Physiology. Online January, 2012. Biyu Jade He, NIH, with Andreas Daffertshofer, VU University, Amsterdam, and Tjeerd Boonstra, University of New South Wales, request contributions to a special issue on this topic, as systems neuroscience increasingly confirms that we personal microcosms indeed possess an archetypal macrocosmos in our brain/minds. See also He, et al, “The Temporal Structures and Functional Significance of Scale-free Brain Activity” in Neuron (66/3, 2010).

The brain is composed of many interconnected neurons that form a complex system, from which thought, behavior, and creativity emerge through self-organization. By studying the dynamics of this network, some basic motifs can be identified. Recent technological and computational advances have led to rapidly accumulating empirical evidence that spontaneous cortical activity exhibits scale-free and critical behavior. These findings may indicate that brain dynamics are always close to critical states – a fact with important consequences for how brain accomplishes information transfer and processing. Capitalizing on analogies between the collective behavior of interacting particles in complex physical systems and interacting neurons in the cortex, concepts from non-equilibrium thermodynamics can help to understand how dynamics are organized. In particular, the concepts of phase transitions and self-organized criticality can be used to shed new light on how to interpret collective neuronal dynamics. (Abstract)

Scale-free dynamics, are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications. (Neuron, 353)

Herculano-Houzel, Suzana, et al. Mammalian Brains are Made of These. Brain, Behavior and Evolution. 86/3-4, 2015. A Dataset of the Numbers and Densities of Neuronal and Nonneuronal Cells in the Brain of Glires, Primates, Scandentia, Eulipotyphlans, Afrotherians and Artiodactyls, and Their Relationship with Body Mass is the long subtitle. A neuroscientist team from Argentina, the United States, and South Africa including Jon Kaas, contribute to mid 2010s advances as our worldwide sapiensphere proceeds to reconstruct and quantify the organismic cerebral encephalization this cognitive ability arose from.

A Dataset of the Numbers and Densities of Neuronal and Nonneuronal Cells in the Brain of Glires, Primates, Scandentia, Eulipotyphlans, Afrotherians and Artiodactyls, and Their Relationship with Body Mass is the long subtitle. A neuroscientist team from Argentina, the United States, and South Africa including Jon Kaas, contribute to mid 2010s advances as our worldwide sapiensphere proceeds to reconstruct and quantify the organismic cerebral encephalization this cognitive ability arose from.

Ishikawa, Masumi, et al, eds. New Developments in Self-Organizing Systems. Neural Networks. 17/8-9, 2004. A large edition dedicated to advances in understanding the brain’s dynamic, hierarchical formation and performance.

Ju, Harang and Danielle Bassett. Dynamic Representations in Networked Neural Systems. Nature Neuroscience. 23/8, 2020. Akin to Muhua Zheng, et al below, University of Pennsylvania neuroscientists delve deeper into our cerebral endowment to find more consistent, layered repositories of knowing inputs and response. Each paper cites dozens of prior references as our collective, 21st project of retrospective self-quantification proceeds to reveal a macro-uniVerse to micro-wumanVerse familial correspondence.

Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these approaches. Here we review the two separate bodies of literature; we next note progress made to join them. We then discuss how patterns of activity evolve from one representation to another, forming dynamic content that unfolds on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission along with exciting frontiers for future research. (Abstract excerpt)

Kahn, D., et al. Dreaming and the Self-Organizing Brain. Journal of Conscious Studies. 7/7, 2000. The authors contend that conceiving the brain as a fractally scaled, critically poised system can bring new understandings and explanations for dream activity and content.

Kaiser, Marcus. A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks. NeuroImage. 57/3, 2011. In an issue on Educational Neuroscience, a Newcastle University neuroinformatics specialist provides an overview of this systems turn to study pervasive interconnections from local neuron and net to global cerebration. And we enter as an example, mid 2011, of how much this “–omics” view is accepted across every natural, organismic, and social realm. However might we imagine a “cosmic connectome” whence the whole genesis uniVerse, or “cosmome” (putting “mom” back again), be rightly appreciated as the animate, generative essence it is?

High-throughput methods for yielding the set of connections in a neural system, the connectome, are now being developed. This tutorial describes ways to analyze the topological and spatial organizations of the connectome at the macroscopic level of connectivity between brain regions as well as the microscopic level of connectivity between neurons. We will describe topological features at three different levels: the local scale of individual nodes, the regional scale of sets of nodes, and the global scale of the complete set of nodes in a network. Such features can be used to characterize components of a network and to compare different networks, e.g. the connectome of patients and control subjects for clinical studies. At the global scale, different types of networks can be distinguished and we will describe Erdös–Rényi random, scale-free, small-world, modular, and hierarchical archetypes of networks. Finally, the connectome also has a spatial organization and we describe methods for analyzing wiring lengths of neural systems. As an introduction for new researchers in the field of connectome analysis, we discuss the benefits and limitations of each analysis approach. (Abstract)

Kaiser, Marcus, et al. Hierarchy and Dynamics of Neural Networks. Frontiers in Neurodynamics. 4/Article 112, 2010. Amongst this burst of cyberscience communications, an Introduction to a special collection that adds further mature credence to how our cerebral anatomy and cognition is formed and functions by self-organizing complexities.

Kanai, Ryota, et al. Cerebral Hierarchies: Predictive Processing, Precision and the Pulvinar. Philosophical Transactions of the Royal Society B. 370/20140169, 2015. In an issue on Cerebral Cartography: A Vision of the Future edited by Semir Zeki, Japanese and British neuroscientists, including Karl Friston, opine that if a brain might best be appreciated as made to predict and plan, then its neural architecture should reflect this. This is a Bayesian brain view which seeks a good enough inference from past experience so as to deal with future happenstance. See also The BRAIN Initiative: A Review by Lyric Jorgenson, et al, Cerebral Cartography and Connectomics by Olaf Sporns, the publication of Cosnciousness: Here, There and Everywhere? by Giulio Tononi and Christof Koch.

The Bayesian Brain: Recent advances in theoretical neuroscience have inspired a paradigm shift in cognitive neuroscience. This shift is away from the brain as a passive filter of sensations towards a view of the brain as a statistical organ that generates hypotheses or fantasies which are tested against sensory evidence. In this formulation, the brain is, literally, a fantastic organ (fantastic: from Greek phantastikos, the ability to create mental images). This perspective can be traced back to Helmholtz and the notion of unconscious inference. This notion has been generalized to cover deep or hierarchical Bayesian inference—about the causes of our sensations—and how these inferences induce beliefs, movement and behaviour. (2) The pulvinar is the largest nucleus in the primate thalamus and has expanded in size during primate evolution—in parallel with other visual structures. The pulvinar has long been thought to play a role in mediating visual attention perhaps by registering the saliency of a visual scene.

Kello, Christopher. Critical Branching Neural Networks. Psychological Review. Online February, 2013. In a lengthy paper that exemplifies the current interdisciplinary reach of science studies from the big bang to big brains, a University of California, Merced, neuroscientist and Acting Dean of Graduate Studies, applies principles from statistical physics to help reveal dynamic cerebral topologies. By this synthesis, a steady scale-invariance from neurons to behavior can be demonstrated. Since cognitive anatomy and process is often poised at a phase transition self-organized criticality, multifractal spikings and webwork geometries are found to result.

It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical branching and, in doing so, simulates observed scaling laws as pervasive to neural and behavioral activity. These scaling laws are related to neural and cognitive functions, in that critical branching is shown to yield spiking activity with maximal memory and encoding capacities when analyzed using reservoir computing techniques. The model is also shown to account for findings of pervasive 1/f scaling in speech and cued response behaviors that are difficult to explain by isolable causes. Issues and questions raised by the model and its results are discussed from the perspectives of physics, neuroscience, computer and information sciences, and psychological and cognitive sciences. (Abstract)

In many different studies of neural and behavioral activity, intrinsic variations have been reported to resemble scaling laws across a wide range of scales (Kello et al., 2010), which are decidedly unlike white noise. (230) These findings add to a vast literature on scaling laws found throughout nature, but their general meaning is an ongoing matter of debate. (230) The same basic debate between domain-specific versus domain-general explanations of scaling laws has been unfolding throughout the sciences for decades, suggestive of a deep issue at stake. If scaling laws are expressions of general principles, then those principles may illuminate relations between neural and behavioral scales of analysis. Relating these scales is a central issue in the psychological and cognitive sciences, and connectionist theories are perhaps the most explicit and formal in addressing this issue to date. (231)

Pervasive Scaling Laws and Criticality Based on results discussed thus far, let us accept for now that scaling laws of various kinds are widely associated with intrinsic variations in neural and behavioral activity and are pervasive to them. If intrinsic variations always reflect inherent component interactions, then what kind of interactions would lead to pervasive scaling laws? Physics provides us with a ready answer in critical phenomena studied in statistical mechanics. The field of statistical mechanics describes how the macroscopic quantities of systems arise statistically from their microscopic components and interactions among them. For some simple systems, interactions can be disregarded because macroscopic quantities are simple aggregates of component quantities, such as heat being an aggregate of particle energies in a gas. (233)

Kello, Christopher, et al. Scaling Laws in Cognitive Sciences. Trends in Cognitive Sciences. Online in Press,, 2010. Senior neuroscientists from the USA, UK, Spain, and Holland find a constant recurrence across neural, behavioral, and linguistic realms to such a degree as to imply a constant, fundamental order in living, complex systems. The same iteration occurs, e.g., in perception, action, memory, and word frequencies. As a consequence, this phenomena must be rooted in and spring from statistical physics principles such as criticality and phase transitions.

Scaling laws are ubiquitous in nature, and they pervade neural, behavioral and linguistic activities. A scaling law suggests the existence of processes or patterns that are repeated across scales of analysis. Although the variables that express a scaling law can vary from one type of activity to the next, the recurrence of scaling laws across so many different systems has prompted a search for unifying principles. In biological systems, scaling laws can reflect adaptive processes of various types and are often linked to complex systems poised near critical points. The same is true for perception, memory, language and other cognitive phenomena. Findings of scaling laws in cognitive science are indicative of scaling invariance in cognitive mechanisms and multiplicative interactions among interdependent components of cognition.

Kelso, J. A. Scott. An Essay on Understanding the Mind. Ecological Psychology. 20/2, 2008. The Florida Atlantic University cognitive systems wizard always has something to say, and this paper in the “Life and the Sciences of Complexity” series in honor of Arthur Iberall, is no exception. Starting with an historical glimpse at the early 1980s, one can glimpse a growing articulation of a deep affinity and synthesis of human and universe.

The central thesis of this article can be stated bluntly: Minds, brains, and bodies, yours and mine, immersed as they are in their own worlds, both outside and inside, share a common underlying dynamics. (183) The remarkable developments of quantum mechanics demonstrating the essential complementarity of both light and matter should have ushered in not just a novel epistemology but a generalized complementary science. (185)

Kelso, Scott. Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge: MIT Press, 1995. A comprehensive theory of cerebral development and cognitive function by way of nonlinear science with an emphasis on synergetic principles.

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