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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Christianson, Nicolas, et al. Architecture and Evolution of Semantic Networks in Mathematics Texts. arXiv:1908.04911. University of Pennsylvania bioneuroengineers NC, Ann Blevins, and Danielle Bassett, with many colleagues, continue to parse the presence of node/link multiplex geometries as they become evident in every natural and social milieu. In this instance, even textual script and its educational content is found to be distinguished. In August 2019, we can report an increasing realization that such a singular, iconic physiology and anatomy is vitally present everywhere. A graphic core-periphery array is depicted with dense inner and sparse outer areas, while another figure cites the same Betti (search) mathematics used to analyze clusters of galaxies. See also The Network Architecture of the Human Brain is Modularly Encoded in the Genome by this team (Bertolero, May 2019). From school books to cerebral faculties and onto to quantome and cosmome phases, a natural genesis is graced by the one, same, ultimately bigender icon.

Knowledge is a network of interconnected concepts. Yet, how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development of learning enhancement strategies. Here we study topological semantic networks reflecting mathematical concepts and their relations in college- linear algebra texts. We find that the networks exhibit strong core-periphery architecture, where a dense core of concepts presented early is complemented with a sparse periphery evenly throughout the exposition. Using tools from applied topology, we find that the expositional evolution of the semantic networks produces and fills knowledge gaps. Broadly, our study lays the groundwork for optimal design principles for textbook teaching in a classroom setting. (Abstract excerpt)

Knowledge has been distilled into formal representations for millennia. Such efforts have sought to explain human reasoning and support artificial reasoning. Semantic networks organize information by detailing concepts (nodes) and their relations (edges), which can be defined by inclusion in the same thesaurus entry, free word association data, or co-occurrence within a corpus of text. Concept maps reflect information in a similar manner, and therefore can be used to evaluate comprehension and identify topics that are most difficult to connect to other concepts. With the capacity to construct semantic networks, and similar formal representations of knowledge comes the challenge of distilling rules and mechanisms of knowledge formalization and acquisition. (1)

Churchill, Nathan, et al. The Suppression of Scale-Free fMRI Brain Dynamics across Three Different Sources of Effort: Aging, Task Novelty and Task Difficulty. Nature Scientific Reports. 6/30895, 2016. A ten person team of research physicians from Canada, United States, and South Korea employ advanced imaging capabilities to quantify and define how much brain anatomies are deeply distinguished by self-similarity. Once again our own neural endowment becomes a microcosm of these universally recurrent patterns and dynamics. By turns then a macrocosmic milieu could then be seen to take on a cerebral guise.

There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P(f ) ∝ f−β, where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD (blood oxygen level dependent) fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent (H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement. (Abstract excerpts)

Fractalness is a ubiquitous property of nature. This scale invariant, self-similar property is used to describe the growth of trees, the formation of mountains, the branching of blood vessels and the crashing of ocean waves. Fractals, as noted by Benoit Mandelbrot (a pioneer in the field of fractal geometry), “are present everywhere.” Fractals are created by repeating a simple process in a recursive way that produces the same pattern over different scales. For example, consider a snowflake’s spatial pattern. As you zoom in on the snowflake with a microscope, the same pattern will be seen as you increase the magnification. Fractalness occurs not only in the spatial domain, but also in the temporal domain, and has been used extensively in the study of brain function. (2)

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.

Cisek, Paul and Ben Hayden. Neuroscience Needs Evolution. Philosophical Transactions of the Royal Society B. December, 2021. University of Montreal and University of Minnesota editors introduce 15 papers in a special Systems Neuroscience through the Lens of Evolutionary Theory issue, in advance of a March 2022 London meeting. But it opens by reminding that Evolution has no goal, no metric aside from the general problem of differential survival. Yet as the content review proceeds, this latest reconstruction of sensory neural capacities back to invertebrate origins well conveys an oriented advance of homologous “elaborations” which fill in, trace and reveal life’s oriented cerebral and cognitive development all the way to our Earthuman retrospect. Into these fraught, terminal 2020s, it is an imperative intent of this Natural Genesis site to help identify and resolve this ecosmic contradiction.

We note these typical entries: Scaffolding Layered Control Architectures through Constraint Closure by Stuart Wilson and Tony Prescott, Evolution of Behavioral Control from Chordates to Primates by Paul Cisek (see review and pg. 4 graphic), An Evolutionary Perspective on Chordate Brain Organization and Function by Thurston Lacalli (herein), Self-Tuition as an Essential Design Feature of the Brain by David Leopold and Bruno Averbeck, The Neuroecology of the Water to Land Transition and the Evolution of the Vertebrate Brain by Malcolm Maclver and Barbara Finlay, and The Evolution of Quantitative Sensitivity by Margaret Bryer, et al.

The nervous system is a product of evolution. As a result, the organization and functions of the brain must be shaped by its history. While not well assimilated into systems neuroscience, this vista can help resolve many mysteries. In this introduction, we survey specific ways that evolutionary theory can enhance cerebral studies. The rest of the theme issue will consider the conservative effect of evolution’s transitional course.

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)

Courellis, Hristos, et al. Abstract representations emerge in human hippocampal neurons during inference.. Nature. August 14, 2024. We enter this work by Cedars-Sinai Medical Center, Los Angeles, CalTech, Columbia University, University of Toronto and New York State Psychiatric Institute neuroscientists for its findings and also to reflect that we Earth peoples may be inherently made and meant to serve as the universe’s way of achieving its own self-description and recognition.

Humans have a cognitive capacity to rapidly adapt to changing environments which draws on an ability to form abstract representations of regularities in the world to support generalization. How these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behavior remains mostly unknown. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representationalfor complex cognition. (Excerpt)

One solution is to encode variables in an abstract format so they can be re-used in new situations to facilitate generalization and compositionality. Here we show that such an abstract representation emerged in the human hippocampus as a function of learning to perform inference. Inferential reasoning is thought to rely on cognitive maps, which have been observed in the hippocampus and underlie inferential reasoning in various complex spatial domains. Here we show that a cognitive map that organizes stimulus identity and latent context in an ordered manner emerges in the hippocampus. (8)

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.

Deco, Gustavo, et al.. The arrow of time of brain signals in cognition. Network Neuroscience. 7/3, 2023. Seven neuroresearchers mainly at the University of Pompeu Fabra, Barcelona and the University of Buenos Aires add a further nonequilibrium thermodynamic dimension to how our awesome, microcosmic cerebral capacity works and plays. See also Combining network topology and information theory to construct representative brain networks in the 5/1 NN issue. Altogether this MIT journal edited by Olaf Sporns has become a premier locus for a range of papers from theoretical work as this to medical diagnostics.

A promising idea in human cognitive neuroscience is that the default mode network (DMN) helps compute and solve task-specific problems. Here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork to assess the flow of events in human brain signals. In turn, this arrow of time is a measure of a nonreversible nonequilibrium mode. Overall, the present thermodynamics-based machine-learning method provides vital new insights into interactions between cognition and the brain in complex environments. (Excerpt)

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)

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