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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Gazzaniga, Michael, ed. The New Cognitive Neurosciences. Cambridge: MIT Press, 2000. A large book by leading authorities covering a wide range of brain development, evolution, and cogitation.

Giusti, Chad, et al. Two’s Company, Three (or More) is a Simplex: Algebraic-Topological Tools for Understanding Higher-Order Structure in Neural Data. arXiv:1601.01704. University of Pennsylvania neuroscientists Giusti and Daniella Bassett, and mathematician Robert Ghrist, combine imaging techniques, network theories, and topological principles to press the frontiers of brain architecture studies. In other project postings this year they are joined by UPs Ann Sizemore (search), Edward Bullmore of Cambridge University, and others: Closures and Cavities in the Human Connectome (1608.03520), Classification of Weighted Networks through Mesoscale Homological Features Journal of Complex Networks (Online August, 2016), Small-World Brain Networks Revisited (1608.05665) and Multi-Scale Brain Networks at (1608.08828). Of further note, the paper advises a use of algebraic topologies, persistent homology, signs of universality, and so on to quantify cerebral faculties. And incredibly these exact phrases also appear in a concurrent, far removed posting by European astronomers about The Topology of the Cosmic Web (1608.0451. search Pranav). What great discovery of a cosmic connectome is arising in our midst?

The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad -- two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes, a theoretical notion developed in the field of mathematics known as algebraic topology, which is now becoming applicable to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. (1601.01704 Abstract)

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large and distributed networks of brain areas, examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates we move from considering exclusively pairwise interactions to capturing higher order relations, considerations naturally expressed in the language of algebraic topology. This provides architecture through which brain network can perform rapid, local processing. Complementary to this study of locally dense structures, we employ a tool called persistent homology to locate cycles, topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. (1608.03520 Abstract)

Graham, Daniel, et al. Network Communication in the Brain. Network Neuroscience. 4/4, 2020. Hobart and William Smith Colleges, Indiana University and McGill University researchers introduce a special issue with this topical subject. Among the dozen entries are Communicability Distance Reveals Hidden Patterns of Alzheimer’s Disease by El Lella and E. Estrada, and Network Topology of the Marmoset Connectome by Z-Q. Liu, et al (abstract below).

Communication models describe the flow of signals among nodes of a network. In neural systems, communication models are increasingly applied to investigate network dynamics across the whole brain, with the ultimate aim to understand how signal flow gives rise to brain function. Communication models range from diffusion-like processes to those related to infectious disease transmission and those inspired by engineered communication systems like the internet. This Focus Feature brings together novel investigations of a diverse range of mechanisms and strategies that could shape communication in mammal whole-brain networks. (Abstract)

Global efforts to understand the emergence of behavior depend on accurate reconstruction of white matter pathways, both in humans and in model organisms. An emerging animal model for applied neuroscience is the common marmoset. A recent open respository by which to systematically study their network architecture is known as the Marmoset Brain Architecture Project. We find evidence of nonrandom organization across multiple scales, including near-minimal path length, multiscale community structure, densely interconnected hubs, a unique motif fingerprint, and the existence of topological cavities. Collectively, these features suggest that the network is configured to support the coexistence of segregation and integration of information. (Abstract, Z-Q Liu, et al)

Grossberg, Stephen. Adaptive Resonance Theory: How a Brain Learns to Consciously attend, Learn, and Recognize a Changing World. Neural Networks. 37/1, 2013. In a lead article for the Twenty-fifth Anniversary Issue (see Kelso also), the Boston University computational neuroscientist, with colleague Gail Carpenter, updates the state of this insightful approach. Our life long neural capacity is seen as much engaged with learning and prediction, by virtue of “complementary cortical streams for attentional recognition and orienting action.” See also in this issue, “Essentials of the Self-Organizing Map” by its founder Teuvo Kohonen, and “Dreaming of Mathematical Neuroscience for a Half a Century” by the pioneer Japanese theorist Shun-ichi Amari.

Grossberg, Stephen. Linking Mind to Brain: The Mathematics of Biological Intelligence. Notices of the American Mathematical Society. 47/11, 2000. The Boston University neural network theoretician considers the deep principles that connect cerebral anatomy and physiology with dynamic streams of thought.

Grosu, George, et al. The fractal brain: scale-invariance in structure and dynamics.. Cerebral Cortex. 33/8, April, 2023. In this primary journal, nine Transylvanian Institute of Neuroscience, Romania, and Merck KGaA, Darmstadt, Germany researchers provide a latest affirmation of an inherent, beneficial self-similarity that seems suffuse and enhance from cerebral to ecosmic realms.

The past 40 years have witnessed extensive research on fractal scale-free dynamics in the brain. Here, we review these concepts across organizational levels from a structural and functional perspective. We discuss evidence about how environmental influences may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we find that behaviors also exhibit scale-free properties which enabling an organism to thrive in a world with the same organizational principles. Altogether these attributes may endow the brain with computational capabilities by which to further unravel how the brain constructs percepts and fosters activities. (Excerpt).

Guastello, Stephen, et al, eds. Chaos and Complexity in Psychology. Cambridge: Cambridge University Press, 2009. Annual conferences of the Society for Chaos Theory in Psychology & Life Sciences, and the pages of its journal Nonlinear Dynamics, Psychology, and Life Sciences, have for some years engaged the fertile application of complex system theories to personal behaviors and group communities. An understanding of their various facets are now sufficiently robust to be gathered in this large volume. Its compass includes an Introduction to Nonlinear Dynamics and Complexity wherein a fractal self-similarity, self-organized criticality, and so on, are found to provide an elusive theoretical explanation for our individual and social lives. (Which indeed offers another example of the natural universality of these genesis phenomena.) Chapters by Paul Van Geert, William Sulis, and Geoff Hollis, et al, are cited herein, along with especial contributions by Terrill Frantz and Kathleen Carley, David Pincus, and Peter Allen which span, e.g., agent-based modeling, psychotherapy, and organizations in evolution.

The theory of complex adaptive systems (CAS) describes the adaptive behavior of living systems as self-organizing, and…. can be used as an overarching framework to study the behavior of living organisms and to incorporate a broad variety of theoretical perspectives from biology, molecular genetics, physics, and chemistry into a single framework that deals in a very broad sense of self-organizing and adaptive behavior. Psychological processes quite naturally have their place within this framework. (Matthijs Koopmans, 521)

Guidolin, Diego, et al. Central Nervous System and Computation. Quarterly Review of Biology. 86/4, 2011. Some two decades since a “parallel distributed processing” or “connectivist” approach was taken up by cognitive science, University of Padova, University of Urbino, Karolinska Institute, and IRCCS San Camillo, neuroscientists survey its status, lately merging in translation with concurrent methods, that try to express how might brains compute thought, and by what digital and analog procedures. Similarly, see also Gualtiero Piccinini and Andrea Scarantino’s “Information Processing, Computation, and Cognition” in the Journal of Biological Physics (37/1, 2011). Both these papers have long bibliographies which widely cover this endeavor.

The brain, therefore, appears characterized by a peculiar combination of computational and noncomputational processes, and could be defined as a dynamically morphing system with computational capabilities of different types, undergoing genetically and environmentally driven self-organization in response to the external context. For this reason, some authors suggested that relevant conceptual frameworks provided by physics, such as statistical mechanics and nonlinear dynamics could represent for theoretical neuroscience particularly suitable tools, likely more helpful than the simple use of concepts from computability theory. (279-280)

Haddad, Wassim, ed. Entropy in Human Brain Networks. www.mdpi.com/journal/entropy/special_issues/brain-network. A topical 2015 collection with this title is edited by the Georgia Tech systems engineer in the online journal Entropy. The quote summarizes its content and aim. Among papers posted by July, we note Fractal Structure and Entropy Production within the CNS (search Seely), Applying Information Theory to Neuronal Networks by Thijs Jung, et al, and Human Brain Networks by Wassim Haddad, et al. In accord with many other disparate contributions this year, a grand unification of human and universe continues apace.

An important area of science where a dynamical system framework of thermodynamics can prove invaluable is in neuroscience. Nonlinear dynamical system theory, and in particular system thermodynamics, is ideally suited for rigorously describing the behavior of large-scale networks of neurons. Merging the two universalisms of thermodynamics and dynamical systems theory with neuroscience can provide the theoretical foundation for understanding the network properties of the brain by rigorously addressing large-scale interconnected biological neuronal network models that govern the neuroelectronic behavior of biological excitatory and inhibitory neuronal networks. As in thermodynamics, neuroscience is a theory of large-scale systems wherein graph theory can be a very useful tool in capturing the connectivity properties of system interconnections, with neurons represented by nodes, synapses represented by edges or arcs, and synaptic efficacy captured by edge weighting giving rise to a weighted adjacency matrix governing the underlying directed graph network topology. The purpose of this special issue is to use a dynamical systems framework merged with thermodynamic state notions to provide a fundamental understanding of the networks properties of the brain. (Synopsis)

Halford, Graeme, et al. Processing Capacity Defined by Relational Complexity. Behavorial and Brain Sciences. 21/4, 1998. Neural nets ought to be considered less in terms of bytes or agents and more about interconnections, their distributed processing. Once again this reciprocity characterizes a complex dynamical system.

Haspel, Gal, et al. To Reverse Engineer an Entire Nervous System. arXiv:2308.06578. A group of 25 senior neuroscientists from across the USA including George Church, Anne Hart, and Konrad Kording propose a next stage for our collective global brain endeavor to retrospectively analyze and describe life's long cerebral evolution. The plan is to use modern machine learning so to first simulate a C. elegans' impressive breadth of brain states and behaviors. And by a philosophia view we wonder what essential kind of existence this may be which proceeds and needs to re-present to itself going forward.

He, Biyu Jade. Scale-Free Brain Activity: Past, Present, and Future. Trends in Cognitive Sciences. Online April, 2014. The NIH neuroscientist provides a succinct review of nature’s constant scale-invariance as dynamically evident in neural anatomy and thought. Her lab website (Google) notes research interests such as “Empirical and theoretical studies to elucidate the neurobiological nature and functional properties of scale-free brain activity.”

Brain activity observed at many spatiotemporal scales exhibits a 1/f-like power spectrum, including neuronal membrane potentials, neural field potentials, noninvasive electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) signals. A 1/f-like power spectrum is indicative of arrhythmic brain activity that does not contain a predominant temporal scale (hence, ‘scale-free’). This characteristic of scale-free brain activity distinguishes it from brain oscillations. Although scale-free brain activity and brain oscillations coexist, our understanding of the former remains limited. Recent research has shed light on the spatiotemporal organization, functional significance, and potential generative mechanisms of scale-free brain activity, as well as its developmental and clinical relevance. (Abstract)

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