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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Cicchetti, Dante and Geraldine Dawson. Editorial: Multiple Levels of Analysis. Development and Psychopathology. 14/417, 2002. An introduction to a special issue to explore how systems neuroscience from genetic to behavioral levels is quantifying a self-organizing brain.

Cocchi, Luca, et al. Criticality in the Brain: A Synthesis of Neurobiology, Models and Cognition. arXiv:1707.05952. Queensland Institute for Medical Research and University of Melbourne neuroscientists including Michael Breakspear post an extensive case for this poised state as the preferred mode of cerebral activity. It opens with evidence of an innate, constant tendency for natural, self-organized complex systems to reach an optimum condition between too much order or chaos. By virtue of this basis, critical neural dynamics can then be connected to and rooted in physical phenomena. Such a balanced accord has been sensed for some years, search Dante Chialvo, this network enters a 2017 affirmation. From a traditional view, one might see a 21st century exemplar of an active, integral balance of these archetypal complements.

Cognitive function requires the coordination of neural activity across many scales, from neurons and circuits to large-scale networks. As such, it is unlikely that an explanatory framework focused upon any single scale will yield a comprehensive theory of brain activity and cognitive function. Modelling and analysis methods for neuroscience should aim to accommodate multiscale phenomena. Emerging research now suggests that multi-scale processes in the brain arise from so-called critical phenomena that occur very broadly in the natural world. Criticality arises in complex systems perched between order and disorder, and is marked by fluctuations that do not have any privileged spatial or temporal scale. We review the core nature of criticality, the evidence supporting its role in neural systems and its explanatory potential in brain health and disease. (Abstract)

Mathematicians and physicist have developed a considerable armory of analytic tools to address multi-scale dynamics in a host of physical, biological and chemical systems. Chief amongst these is the notion of criticality, an umbrella term that denotes the behavior of a system perched between order and disorder. A critical systems shows scale-free fluctuations that stretch from the smallest to the largest scale. Despite their apparent random nature, the fluctuations in these systems are highly structured, obeying deep physical principles that show commonality from one system to the other (so-called universality). (4)

Costa, Ariadne, et al. Fractal Analyses of Networks of Integrate-and-Fire Stochastic Spiking Neurons. arXiv: 1801.08087. We note because Indiana University and University of Central Florida neuroscientists including Olaf Sporns cite additional evidence for the brain’s critically-poised functional states, along with resultant (multi) fractal, self-similar geometries that they exhibit.

Although there is increasing evidence of criticality in the brain, the processes that guide neuronal networks to reach or maintain criticality remain unclear. The present research examines the role of neuronal gain plasticity in time-series of simulated neuronal networks composed of integrate-and-fire stochastic spiking neurons, and the utility of fractal methods in assessing network criticality. Fractal scaling was greatest in networks with a mid-range of neuronal plasticity, versus extremely high or low levels of plasticity. Peak fractal scaling corresponded closely to additional indices of criticality, including average branching ratio. Networks near critical states exhibited mid-range multifractal spectra width and tail length, which is consistent with literature suggesting that networks poised at quasi-critical states must be stable enough to maintain organization but unstable enough to be adaptable. (Abstract excerpts)

Damasio, Antonio. Looking for Spinoza: Joy, Sorrow and the Feeling Brain. Orlando, FL: Harcourt, 2003. The neuroscientist and author finds in Spinoza’s writings a prescience of how the corporeal body affects the emotional brain. Some three centuries later, Damasio describes this process as a branching tree from metabolism and basic reflexes through pain and pleasure behaviors to drives, motivations and on to personal and social emotions. These layers are seen to occur by a ‘nesting principle,’ whereby the overall body/brain homeostatis and homeodynamics takes on the form of a self-similar fractal. So once again our bodily and cerebral vitality expresses the universal system at work.

Di Ieva, Antonio, ed.. The Fractal Geometry of the Brain. New York: Springer, 2016. A Macquarie University, Sydney neuroscientist (search) collects the latest research in a dedicated volume on how much cerebral dynamics are graced and facilitated by nested self-similar topologies, aka “fractalomics.” Typical chapters are Does a Self-Similarity Logic Shape the Organization of the Nervous System? by Diego Guidolin, et al and The Fractal Geometry of the Human Brain: An Evolutionary Perspective by Michel Hofman (both Abstracts next). We also want to record parallel efforts by Luigi Agnati (search) and colleagues.

From the morphological point of view, the nervous system exhibits a fractal, self-similar geometry at various levels of observations, from single cells up to cell networks. From the functional point of view, it is characterized by a hierarchic organization in which self-similar structures (networks) of different miniaturizations are nested within each other. On this basis, the term “self-similarity logic” was introduced to describe a nested organization where at the various levels almost the same rules (logic) to perform operations are used. Thus, they can represent key concepts to describe its complexity and its concerted, holistic behavior. (Abstract excerpt, Guidolin, et al)

The evolution of the brain in mammals is characterized by changes in size, architecture, and internal organization. Also the geometry of the brain, and the size and shape of the cerebral cortex, has changed notably during evolution. Comparative studies of the cerebral cortex suggest that there are general architectural principles governing its growth and development. In this chapter some design principles and operational modes that underlie the fractal geometry and information processing capacity of the cerebral cortex in primates, including humans, will be explored. (Abstract, M. Hofman)

Di Ieva, Antonio, et al. Fractals in the Neurosciences. Part I: General Principles. The Neuroscientist. 20/4, 2014. Brain researchers with posts in Canada, Austria, Spain, Poland and Venezuela offer a good review to date of realizations that nature’s ubiquitous recurrence of the same forms everywhere indeed also braces and graces our cerebral faculty, as it facilitates our constant cognitive activities.

The natural complexity of the brain, its hierarchical structure, and the sophisticated topological architecture of the neurons organized in micro-networks and macro-networks are all factors that limit an Euclidean geometry and linear dynamics. The introduction of fractal geometry for the quantitative description of complex natural systems has been a major paradigm shift. Modern neurosciences admit the prevalence of fractal properties such as self-similarity in the brain at molecular, anatomic, functional, and pathological levels. Fractal geometry is a mathematical model that offers a universal language for neurons and glial cells as well as the whole brain with its physiopathological spectrums. For a holistic view of fractal geometry of the brain, we review here the basic concepts and main applications. (Abstract excerpts)

Di Leva, Antonio, et al. Fractals in the Neurosciences. The Neuroscientist. 20/4, 2014. With Fabio Grizzi, Herbert Jelinek, Andras Pellionisz, and Gabriele Losa, theorists from Canada, Austria, Italy, Abu Dhabi, Australia, Switzerland and the USA who have previously proposed that such a topological “recursion” graces brain anatomy, physiology, and cognition since the 1990s here confirm its distinctive and effective presence.

The natural complexity of the brain, its hierarchical structure, and the sophisticated topological architecture of the neurons organized in micronetworks and macronetworks are all factors contributing to the limits of the application of Euclidean geometry and linear dynamics to the neurosciences. The introduction of fractal geometry for the quantitative analysis and description of the geometric complexity of natural systems has been a major paradigm shift in the last decades. Nowadays, modern neurosciences admit the prevalence of fractal properties such as self-similarity in the brain at various levels of observation, from the microscale to the macroscale, in molecular, anatomic, functional, and pathological perspectives. Fractal geometry is a mathematical model that offers a universal language for the quantitative description of neurons and glial cells as well as the brain as a whole, with its complex three-dimensional structure, in all its physiopathological spectrums. For a holistic view of fractal geometry of the brain, we review here the basic concepts of fractal analysis and its main applications to the basic neurosciences. (Abstract)

Dixon, James, et al. Multifractal Dynamics in the Emergence of Cognitive Structure. Topics in Cognitive Science. Online October, 2011. With John Holden, Daniel Mirman, and Damian Stephen, now at Harvard, further quantifications of how “cognition is characterized by seamless interactions among multiple scales of organization (e.g., chemical, physiological, and environmental).”

The complex-systems approach to cognitive science seeks to move beyond the formalism of information exchange and to situate cognition within the broader formalism of energy flow. Changes in cognitive performance exhibit a fractal (i.e., power-law) relationship between size and time scale. These fractal fluctuations reflect the flow of energy at all scales governing cognition. Information transfer, as traditionally understood in the cognitive sciences, may be a subset of this multiscale energy flow. The cognitive system exhibits not just a single power-law relationship between fluctuation size and time scale but actually exhibits many power-law relationships, whether over time or space. (1)

Doerig, Adrian, et al. The Neuroconnectionist Research Programme. arXiv:2209.03718. Some four decades after its 1980s origins, eleven senior scientists with postings in the Netherlands, Germany, Canada, and the USA including Jeann Ismael and Konrad Kording seek to advance this broad program into the 2020s as a proven method to explain the emergence of cognitive phenomena, behavior and neural data from bio-inspired, yet simple distributed coding principles. Over the years it has served beneficial usage in vision, audition, semantics, language, reading studies and brain coding principles. Our “planatural philosophia” is to appreciate its ascent to a global facility which has commenced to learn on her/his own, as newly sourced in a PedpaPedia Earthica edition.

Artificial Neural Networks (ANNs) inspired by biology are now widely used to model behavioral and neural data, an approach we identify as neuroconnectionism. ANNs are good tor information process studies in the brain, but less so for cognitive functions. Here we take a philosophy of science view of a cohesive ANN research programme with a computational language for falsifiable theories about brain computation. By some timeline, we review past and present projects to conclude the neuroconnectionist approach is highly progressive, and can generate novel insights into our cerebral endeavors. (Abstract excerpt)

Dogaru, Radu. Universality and Emergent Computation in Cellular Neural Networks. River Edge, NJ: World Scientific, 2003. A technical book on recurrent elemental cells that interact or “collaborate” in a nested fashion from which emerges an image or thought. In translation, what is again found is the universal creative system in its node and link, agent and relation (male and female) complementarity.

A similar hierarchical and cellular organization is revealed in language, which could be considered as “image of our brains.” Basic cells (phonemes or characters in the written language) collaborate in an emergent manner to form words, which then are again combined in phrases and so on providing a structured reflection of the outer world.
Recapitulating, universal characteristics of our world can be captured in the emergent dynamics of a cellular model where a population of similar cells exchanges information locally with the cells in their neighborhood. (1)

Donahoe, John and Vivian Packard Dorsel, eds. Neural-Networks Models of Cognition. Amsterdam: Elsevier Science, 1997. Self-organizing neural nets guide brain development, plasticity, perception, stimuli responses, reinforcement learning, and complex behavior.

Dresp-Langley, Birgitta. Seven Properties of Self-Organization in the Human Brain. Big Data and Cognitive Computing.. 4/2, 2020. In this MDPI online journal, a CNRS University of Strasbourg, France research director provides an extensive survey of how nature’s proclivity to organize itself so distinguishes our cerebral development and cognitive abilities.

The principle of self-organization has become a significant part of the emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology, medicine, ecology, and sociology. In regard, there are (at least) seven key properties of self-organization identified in brains: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) resiliency, 5) plasticity, 6) local-to-global arrangement, and 7) dynamic system growth. These are defined here via insights from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (S. Grossberg), and from physics to show that self-organization achieves functional stability and plasticity with minimum complexity. (Abstract excerpt)

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