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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VII. WumanKinder: An EarthSphere Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Betzel, Richard. Organizing Principles of Whole-Brain Functional Connectivity in Zebrafish Larvae. Network Neuroscience. 4/1, 2020. An Indiana University neuropsychologist extends and applies the research advances that this MIT journal conveys about overall brain anatomy and physiology to this aquatic scale so as to find the same, analogous cognitive formations in effect across this middle meso-scale domain.

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized such as geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we study whole-brain networks recorded in larval zebrafish which show that connections are distance-dependent and that networks exhibit a hierarchical modularity. Spontaneous network structure is also found to constrain stimulus-evoked net reconfigurations which are highly consistent across individuals. Thus, basic organizing principles of whole-brain functional brain networks are in effect at the mesoscale. (Abstract excerpt)

Betzel, Richard, et al. Multi-Scale Community Organization of the Human Structural Connectome and its Relationship with Resting-State Functional Connectivity. Network Science. 1/3, 2013. As an ascendant planetary progeny proceeds to quantify, explain and discover a genesis universe, in this new Cambridge online journal, a team from Indiana University and the University of Lausanne, including Olaf Sporns, offer a capsule of current progress. The paper opens by citing how a decade of studies of natural network phenomena have led to the distillation of an independent, universally applicable, description. In regard, human brains, via intricate, scintillating connections, can be seen as a premier exemplar. So we peoples do indeed possess an archetypal microcosm of this macro cosmome. Compare with Tognoli, Tononi, Rubinov, many other entries as they verge on a grand synthesis.

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. In this report, we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scale. (Abstract)

Bota, Mihail, et al. From Gene Networks to Brain Networks. Nature Neuroscience. 6/8, 2003. A proposal to apply similar techniques used in the study of the genome to comprehend neural processes. A prototype Brain Architecture Knowledge Management System is described akin to bioinformatics to help sort out nomenclature issues and to express their dynamic interconnections.

Botvinick, Matthew. Hierarchical Models of Behavior and Prefrontal Function. Trends in Cognitive Sciences. 12/5, 2007. The Princeton psychologist in this article, and others accessible from his website, professes an isomorphic alignment between behavioral scales and their neural representation as mind and active manifestation come to mirror each other.

Breakspear, Michael and Cornelis Stam. Dynamics of a Neural System with a Multiscale Architecture. Philosophical Transactions of the Royal Society B. 360/1051, 2005. Based on extensive neuroimaging studies of cerebral connectivity, the human brain is found to be arranged as a nest of self-similar modules and interactive networks. This composition is said to reflect nature’s newly discovered universal scaling laws. By these advances, one can add that human brains appear to exhibit the same pattern and process as life’s evolutionary genesis and the organic universe from which they emerge. A significant paper.

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales – neurons, minicolumns, cortical columns, functional brain regions, and so on. (1051) A multiscale hierarchy of such neuronal ensembles – with interdependences between scales – is then constructed using the wavelet transform…this yields a hierarchy of neuronal systems whose spatial scales are geometrically nested… (1052)

Buckner, Randy and Fenna Krienen. The Evolution of Distributed Association Networks in the Human Brain. Trends in Cognitive Sciences. 17/12, 2013. In work noted by the New York Times as “In the Human Brain, Size Really Isn’t Everything” (December 26, 2013), Harvard University and Massachusetts General Hospital neuroscientists propose that as earlier, rudimentary neural architectures became “untethered,” this allowed more complex connective topologies to form in primate and homo sapiens brains. A volumetric increase is not enough, an increasingly intricate “connectome” makes the difference. And of further importance, as per the quote, neural evolution is seen in an embryonic way as a expansive deployment from an original archetypal geometry.

The inherited constraints of development and the general plan, or Bauplan, of the brain are powerful limiters on how neural circuits can evolve across generations. Here we raise the possibility that critical features of the association cortex, linked to size scaling, may contribute to the human brain’s extraordinary capabilities. The central idea is that a distributed form of circuit may have become increasingly prominent when ancient rules of development were expressed in an expanding cortical mantle. The possibility that simple mechanisms play a major role in recent brain evolution is comforting because it demystifies the gap between our brain’s capabilities and those of our ancestors. (661-662)

Bullmore, Edward, et al. Generic Aspects of Complexity in Brain Imaging Data and other Biological Systems. Neuroimage. 47/3, 2009. University of Cambridge, NIH, and University of Melbourne neuroscientists, as the extended quotes aver, contend that after a decade of intensifying progress in endeavors to view neural anatomy and activity as an iterative, emergent self-organization, as every other domain of life and society has done also, that a universality of such similar kind can be admitted and appreciated. Upon reflection, this is huge for we are now invited to realize in our midst an epochal worldwide discovery of an untangled, comprehensible nature, arising from and manifestly representing a true universe to human genesis.

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. (Abstract, 1125)

However, it is becoming increasingly clear that complexity may be a characteristic of biological systems in general; and that some of the same mathematical tools and concepts can appropriately be used to quantify and compare aspects of complexity in substantively very diverse systems. To take a single, illustrative example in brief; many complex systems have been shown to have a modular or nearly-decomposable organization, including systems as different as the human brain transcriptome, the global air transportation network, and ecological or economic networks. (1126)

The important generalization is that one way forward in dealing with the formidable complexity of the human brain may be to recognize that certain key principles of its organization are shared in common with other complex systems in biology and elsewhere. This idea that both brain and biological systems may have generic properties in common is one implication of the more general universality hypothesis: that certain network organizing principles are highly conserved and more-or-less universally instantiated in real-life networks. Studies addressing network organization have proliferated recently in an interdisciplinary research area, which is driven largely by technical developments in statistical physics and has begun to demonstrate a startling degree of commonality in the organization of substantively distinct complex systems. (1126)

Buzsaki, Gyorgy. Neural Syntax: Cell Assemblies, Synapsembles, and Readers. Neuron. 68/3, 2011. Natural realms from quanta and genomes to cooperative groups are presently being reinterpreted and better understood not only by complex systems science, but by way of novel appreciations of their linguistic, semiotic essence. As the quotes note, the Rutgers University behavioral neuroscientist (search) proceeds with a similar approach to parse cerebral structure and dynamics in such grammatical terms. See also Buzsaki's 2006 book Rythmns of the Brain for more. And what might we take from this – is a greater creation being found that is, as our traditions know, deeply textual, poetic, even scriptural, in kind?

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as ‘‘cell assemblies,’’ underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by ‘‘reader-actuator’’ mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights (‘‘synapsembles’’). (Abstract, 362)

Neural Syntax: Rules that Integrate and Parse Fundamental Assemblies. In general, syntax (grammar) is a set of principles that govern the transformation and temporal progression of discrete elements (e.g., letters or musical notes) into ordered and hierarchical relations (e.g., words, phrases, sentences or chords, chord progression, and keys) that allow for a congruous interpretation of the meaning of language or music by the brain. (365) Neural Words and Sentences. The second hypothesis of this review is that temporal sequencing of discrete assemblies by neural syntax can generate neural words and sentences. Although strings of assemblies can be regarded simply as a larger assembly, and indeed assemblies of different length and size refer to many things in neuroscience, I chose the term ‘‘neural word’’ to emphasize that words consist of multiples of the fundamental assemblies. (365)

Buzsaki, Gyorgy. Rhythms of the Brain. Oxford: Oxford University Press, 2006. A Hungarian-American professor of Molecular and Behavioral Neuroscience at Rutgers University informs and enriches this endeavor by the principles of nonlinear science, via theory and experiment, to articulate an innately self-organizing cerebral formation and activity. By this vista, our brains, distinguished by a universal pattern and process across nested scales, can be appreciated to embody nature’s independent while emergent dynamics. This intricate volume, graced not by chapters but “cycles,” joins human and universe, and goes on to suggest a rudimentary global brain may be likewise generating itself. But a disclaimer is added on page 4 that Nature, of course, has no laws, desires, goals, or drives. (Is this necessary for publication or membership?) But Buzsaki and colleagues make a major contribution to a genesis vision, which is worth extended excerpts.

Oftentimes, not only does complexity characterize the system as a whole, but also its constituents (e.g. neurons) are complex adaptive systems themselves, forming hierarchies at multiple levels. All these features are present in the brain’s dynamics because the brain is also a complex system. (11) The scale invariance of fractals implies that knowledge of the properties of a model system at any scale can be used to predict the structure of the real system at larger or smaller scales. Applying this knowledge to neuroscience, knowing the fundamental properties of the organization of the cerebral cortex in any mammalian species and the rules of network growth, the principal structural organization of smaller and larger brains can be predicted. (30)

In essence, the claim is that a collective pattern recorded from a small portion of the cortex looks like the pattern recorded from the whole. This “scale invariance” or “self-similarity” is a decisive characteristic of fractals. Fractal structures – such as river beds, snow flakes, fern leaves, tree arbors, and arteries – and fractal dynamic processes – such as pink noise, cloud formation, earthquakes, snow and sand avalanches, heart rhythms, and stock market price fluctuations – are self-similar in that any piece of the fractal design contains a miniature of the entire design. Regarding the collective behavior of neuronal signals as fractals with self-similar fluctuations on multiple time and geometry scales has potentially profound theoretical and practical implications for understanding brain physiology. (126-127) The concept that physical systems, made up of a large number of interacting subunits, obey universal laws that are independent of the microscopic details is a relative recent breakthrough in statistical physics. Neuroscience is in serious need of a similar systematic approach that can derive mesoscale laws at the level of neuronal systems. (127)

Buzsaki, Gyorgy, et al. Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms. Neuron. 80/3, 2013. In a “Neuroscience Retrospective” issue, Buzsaki, NYU, Nikos Logothetis, MPI Biological Cybernetics, and Wolf Singer, MPI Brain Research, contribute a current summary and bibliography for this reconception, as many other fields, by way of nonlinear complexities. By these theories, a mature synthesis and verification has been reached of a unified cerebral architecture and activity graced by nested, self-organized critical dynamics. Please search Buzsaki and Singer for prior papers.

Despite the several-thousand-fold increase of brain volume during the course of mammalian evolution, the hierarchy of brain oscillations remains remarkably preserved, allowing for multiple-time-scale communication within and across neuronal networks at approximately the same speed, irrespective of brain size. Deployment of large-diameter axons of long-range neurons could be a key factor in the preserved time management in growing brains. We discuss the consequences of such preserved network constellation in mental disease, drug discovery, and interventional therapies. (Abstract)

We hypothesize below that the aforementioned essential features of brain organization, the activity-information retention and the local-global integration, are maintained by a hierarchical system of brain oscillations, and we demonstrate that despite a 17,000-fold variability in brain volume across mammalian species, the temporal dynamics within and across brain networks remain remarkably similar. It follows that, irrespective of brain size, the management of multiple time-scales is supported by the same fundamental mechanisms, despite potential adaptive changes in network connectivity. (751)

Cabessa, Jeremie and Hava Siegelmann. The Computation Power of Interactive Recurrent Neural Networks. Neural Computation. 24/4, 2012. University of Massachusetts, Amherst, computational neuroscientists take these cerebral complexities to exemplify how nature evolves, develops and learns. We are then invited to realize that the same dynamical trial and error, feedback to move forward, iterative process is in effect everywhere. See also Turing on Super-Turing and Adaptivity by Hava Siegelmann in Progress in Biophysics and Molecular Biology (113/117, 2013), and search Richard Watson 2014 herein.

In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities. (Abstract)

This analog information processing model turns out to be capable of capturing the nonlinear dynamical properties that are most relevant to brain dynamics. (997) Indeed, in the brain (or in organic life in general), information is processed in an interactive way, where previous experience must affect the perception of future inputs and older memories themselves may change with response to new inputs. Hence, neural networks should be conceived as performing sequential interactions or communications with their environments and be provided with memory that remains active throughout the whole computational process. Accordingly, we propose to study the computational power of recurrent neural networks from the rising perspective of interactive computation. (997)

Carruthers, Peter. Practical Reasoning in a Modular Mind. Mind & Language. 19/3, 2004. (As an initial note, it appears that “modular” schools also exist in cognitive science with various persuasions and viewpoints.) Philosopher Carruthers makes a case for domain-specific modules in the brain which arose in evolution in response to changing environments. In support of an evolutionary psychology, they are now seen to influence the mores of human behavior.

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