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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

2. Systems Neuroscience: Multiplex Networks and Critical Function

Psujek, Sean, et al. Connection and Coordination: The Interplay Between Architecture and Dynamics in Evolved Model Pattern Generators. Neural Computation. 18/3, 2006. The same complex network geometries that occur throughout nature are present in neural systems, in this case with regard to simulation of a walking task via neuron excitabilities and connections.

From molecules to cells to animals to ecosystems, biological systems are typically composed of large numbers of heterogeneous nonlinear dynamical elements densely interconnected in specific networks. (729)

Pu, Jiangbo, et al. Developing Neuronal Networks: Self-Organized Criticality Predicts the Future. Nature Scientific Reports. 3/1081, 2013. Britton Chance Center for Biomedical Photonics, Wuhan National Lab for Optoelectronics, Huazhong University of Science and Technology, systems neuroscientists again discern and confirm how nature’s universal creativity similarly graces our cerebral anatomy, physiology, and consequent thought patterns and processes. And since these phenomena appear to have an apparently independent, dynamic sequence, the forward course of self-organizing cerebration augurs toward potential future states.

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitromaturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and ‘‘aging’’ process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future. (Abstract)

Our data suggest that a self-organized criticality mechanism with long range interactions hereby plays a potential role in the emergence of metastable activity states in an evolving network. In temporally evolving networks, the coexistence of self-organized criticality and metastable state transition showed in our results provides an unprecedented experimental evidence for the hypothesis that critical networks should simultaneously exhibit criticality and metastability. Understanding the self-organized nature of developing networks may hold the key to elucidating the network-level mechanisms of brain development. Based on our result, it may be possible to predict how the network will evolve by examining the criticality in early stages. It will open a door to the investigation of age-related neuronal dysfunction, and ultimately to the forecasting of developmental dynamics of the brain. (5)

Raghavan, Guruprasad and Matt Thomson. Neural Networks Grown and Self-Organized by Noise. arXiv:1906.01039. We cite this entry by Caltech bioengineers for the way it implies an internal drive and direction that is an intelligence gaining, self-learning, quickening genesis. As these observation grow in breadth and veracity, they suggest a natural presence that seems to require at some far point the achieve its own witness and affirmation.

Living neural networks in the brain perform an array of computational and information processing tasks including sensory input processing, storing and retrieving memory, decision making, and more globally, generate the general phenomena of “intelligence”. In addition to their information processing feats, brains are unique because they are computational devices that actually self-organize their intelligence. In fact brains ultimately grow from single cells during development. Engineering has yet to construct artificial computational systems that can self-organize their intelligence. In this paper, inspired by neural development, we ask how artificial computational devices might build themselves without human intervention. (1)

Ramus, Franck. Genes, Brain, and Cognition. Cognition. 101/2, 2006. An introduction to a special issue on the interdisciplinary juncture and cross-fertilization of these often removed domains, which are seen to be at mid-points in both directions.

Rizzolatti, Gaicomo, et al. Mirrors in the Mind. Scientific American. November, 2006. With co-authors are Leonardo Fogassi and Vittorio Gallese, all from the Department of Neuroscience, University of Padua, a popular introduction to the discovery of mirror neurons in the brain, which are activated either when a person is performing an action, or observing another doing the same. Their importance is just beginning to be appreciated for the evolution and enhancement of primate and human sociality, along with language development and other psychological advances. By this attribute, human persons are inherently wired for and linked in social behavior. A deficit or absence of this capability may then be a cause of autism.

Further publications and resources can be accessed by searching Google for the author’s name, which reaches their website, or for the phrase ‘mirror neuron.’ A technical source is Rizzolatti, G. and L. Craighero. The Mirror-Neuron System. Annual Review of Neuroscience. 27/169, 2004. Such an attribute is also noted in psychologist Daniel Goleman’s new book Social Intelligence: The New Science of Human Relationships. (New York: Bantam Books, 2006).

Rockwell, W. Teed. Neither Brain nor Ghost. Cambridge: MIT Press, 2005. An attempt to move beyond the Cartesian duality of matter and mind via connectionism and dynamic systems theory.

Rubinov, Mikail, et al. Symbiotic Relationship between Brain Structure and Dynamics. BMC Neuroscience. 10/55, 2009. In this British online journal, an international team from Australia, Japan and the United States, including Olaf Sporns, provide a summary to date of the worldwide nonlinear revolution as collaborative humankinder retrospectively quantifies the personal human brain anatomy, physiology, and function from whom it arose.

Brain structure and dynamics are interdependent through processes such as activity-dependent neuroplasticity. In this study, we aim to theoretically examine this interdependence in a model of spontaneous cortical activity. To this end, we simulate spontaneous brain dynamics on structural connectivity networks, using coupled nonlinear maps. On slow time scales structural connectivity is gradually adjusted towards the resulting functional patterns via an unsupervised, activity-dependent rewiring rule. The present model has been previously shown to generate cortical-like, modular small-world structural topology from initially random connectivity. (Background) Our results outline a theoretical mechanism by which brain dynamics may facilitate neuroanatomical self-organization. We find time scale dependent differences between structural and functional networks. These differences are likely to arise from the distinct dynamics of central structural nodes. (Conclusion)

Modular small-world network topology may represent a basic organizational principle of neuroanatomical connectivity across multiple spatial scales [1-6]. Small-world networks are clustered (like ordered networks), and efficiently interconnected (like random networks) [1]. Modular networks are characterized by the presence of highly interconnected groups of nodes (modules) [7]. Hence a modular small-world connectivity reconciles the opposing demands of segregation and integration of functionally specialized brain areas [8] in the face of spatial wiring constraints [9]. (2)

Sanborn, Adam and Nick Chater. Bayesian Brains without Probabilities. Trends in Cognitive Sciences. Online March, 2017. University of Warwick and Warwick Business School behavioral neuroscientists finesse this popular turn to explain cognitive behavior as iterative process of likely probabilities. Rather than just better guesses, our cerebrations are seen to repeatedly survey an array of candidate or sample options, from which choices are made.

Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy. (Abstract)

Bayesian sampler: an approximation to a Bayesian model that uses a sampling algorithm such as MCMC to avoid intractable integrals. While the model is used to perform Bayesian inference, the sampling algorithm itself is simply a mechanism for producing samples. Deep belief network: a hierarchical artificial neural network of binary variables. Each layer of the network can be composed on simpler networks such as Boltzmann machines. Markov chain Monte Carlo: a family of algorithms for drawing samples from probability distributions. These algorithms transition from state to state with probabilities that depend only on the current state. The transition probabilities are carefully chosen so that the states are (dependent) samples of a target probability distribution. (Glossary)

Saxe, Andrew, et al. A Mathematical Theory of Semantic Development in Deep Neural Networks. Proceedings of the National Academy of Sciences.. 116/11537, 2019. In a highly technical article, AS, Oxford University, James McClelland, Stanford University (original developer with David Rumelhart of Parallel Distributed Processing in the 1980s), and Surya Ganguli, Google Brain, CA advance this machine to brain revolution so as to better organize and encode knowledge by means of typicality and category coherence, optimal learning, invariant similarities and more. See also Evolution of Scientific Networks in Biomedical Texts at arXiv:1810.10534 and Human Information Processing in Complex Networks at 1906.00926.

An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge. These results raise a fundamental question: what are the principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge? We address this by analyzing the nonlinear dynamics of learning in deep linear networks. We find solutions to these learning dynamics that explain disparate phenomena in semantic cognition such as the hierarchical differentiation of concepts through developmental transitions, the ubiquity of semantic illusions between transitions, the emergence of category coherence which controls the speed of semantic processing, and the conservation of semantic similarity in neural representations across species. Our simple neural model can thus recapitulate diverse regularities underlying semantic development, while providing insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics results in these regularities. (Abstract edits)

Schmidhuber, Jurgen. Deep Learning in Neural Networks: An Overview. Neural Networks. 61/2, 2015. A technical tutorial by the University of Lugano, Switzerland expert upon advances in artificial or machine learning techniques, based on how our own brains think. Sophisticated algorithms, multiple processing layers with complex structures, assignment paths, non-linear transformations, and so on are at work as they refer new experiences to prior representations for comparison. See also, for example, Semantics, Representations and Grammars for Deep Learning by David Balduzzi at arXiv:1509.08627. Our interest recalls recent proposals by Richard Watson, Eors Szathamary, et al to appreciate life’s evolution as quite akin to a neural net, connectionist learning process.

Sendhoff, Bernhard, et al, eds. Creating Brain-Like Intelligence from Basic Principles to Complex Intelligent Systems. Berlin: Springer, 2009. (Lecture Notes in Artificial Intelligence LNAI 5436) Sendhoff and co-editors Olaf Sporns and Edgar Korner lead off with a chapter on “From Complex Networks to Intelligent Systems.” The work is a mature example of how cerebral and cognitive studies have morphed to this dynamical approach, just as systems biology/genetics has done. From the quotes, please note the same ubuntu, creative union of semi-autonomy and integration as everywhere else, whose ubiquity quite implies, and springs from a common, mathematical source.

The accumulation of ever more detailed biological, cognitive and psychological data cannot substitute for general principles that underlie the emergence of intelligence. It is our belief that we have to more intensively pursue research approaches that aim at a holistic and embedded view of intelligence from many different disciplines and viewpoints. (4) The aim of theoretical neuroscience is to understand the general principles behind the organization and operation of nervous systems. (4)

The brain is a complex system because it consists of numerous elements that are organized into structural and functional networks which in turn are embedded in a behavior and adapting organism. Brain anatomy has long attempted to chart the connection patterns of complex nervous systems, but only recently, with the arrival of modern network analysis tools, have we been able to discern principles of organization within structural brain networks. One of the overarching structural motifs points to the existence of segregated communities (modules) of brain regions that are functionally similar within each module and less similar between modules. (5)

Seung, Sebastian. Connectome: How the Brain’s Wiring Makes us Who We Are. Boston: Houghton Mifflin Harcourt, 2012. A MIT computational neuroscientist provides an accessible entry to imaginations and expansions of everything neural and cognitive in a similar genre to genome networks. Main sections of Connectionism, Nature and Nurture, Connectomics, and Beyond Humanity, well cover these frontiers. In closing, a "transhumanism" is proposed that would implement these advances as a way to recover meaningful lives now impoverished by Stephen Weinberg’s “pointless” science.

In the same way, a connectome is the totality of connections between the neurons in a nervous system. The term, like genome, implies completeness. A connectome is Not one connection, or even many. It is all of them. (xiii)

The Bible said that God made man in his own image. The German philosopher Ludwig Feuerbach said that man made God in his own image. The transhumanists say that humanity will make itself into God. (273)

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