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VII. Our Earthuman Ascent: A Major Evolutionary Transition in Twndividuality2. Systems Neuroscience: Multiplex Networks and Critical Function Liang, Junhao and Changsong Zhou. Criticality Enhances the Multilevel Reliability of Stimulus Responses in Cortical Neural Networks. PLOS Computational Biology.. January, 2022. We cite this entry by Beijing-Hong Kong-Singapore, Joint Centre for Nonlinear and Complex Systems researchers for one more notice of nature’s prime preference to seek and reside at an optimum balance between two opposite but reciprocal states. Our especial is a tendency toward Chimera-like active or static modes. The complexity of dynamical brain activity ranges from neuronal spiking and neural avalanches to oscillatory potentials of local circuits across many states. Such multilevel variables are functionally and behaviorally relevant. To better clarify, we study the stimulus–response of neural circuits. Our model assumes excitation–inhibition (E–I) interactions and synaptic couplings with a critical sub-region. We further analyze the nonlinear dynamical principles using a novel and a broadly applicable mean-field theory. (Summary excerpt) Lindsay, Grace. Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.. London: Bloomsbury Sigma, 2021. An Assistant Professor of Psychology and Data Science at New York University begins her book with a review of mathematic and physical principles so they can be applied to cognitive functions such as memories, vision, decision making, excitation/inhibition. With this currency in place, some Grand Unified Theories are surveyed such as Karl Friston’s free energy, Jeff Hawkins’ Thousand Brains project, and Giulio Tononi’s integrated information model. And one wonders whomever is this late planetary faculty as it proceeds to learns on its own. What manner of multiuniverse needs to form a midway self-representation, realization and participatory affirmation? In Models of the Mind, computational neuroscientist Grace Lindsay explains how mathematical models have allowed researchers to understand and describe many of the brain's processes such as decision-making, sensory processing, stored memory, and more. Each chapter focuses on mathematical tools that have been applied from the individual neuron to their many interactions, whole brain areas and the consequent behaviours. In addition, Grace examines the history of the field from the eighteenth century and to the large models of neural networks that form the basis of modern artificial intelligence. Lofti, Nastaran, et al. Statistical Complexity is Maximized Close to Criticality in Cortical Dynamics. arXiv:2010.040123. Nine Brazilian neuroscientists contribute to a growing notice that cerebral activity tends and prefers to reside in this optimum balance. See also Quasicritical Brain Dynamics by Leandro Fosque, et al at 2010.02938 and Testing the Critical Brain Hypothesis using a Phenomenological Renormalization Group by Giorgio Nicoletti, et al at 2001.04353 for further work. Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity to desynchronized and disordered regimes. It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. We show that statistical complexity is maximized close to criticality for cortical spiking data, as well as for a network model of excitable elements at a critical point of a non-equilibrium phase transition. (Abstract excerpt) Lynn, Christopher, et al. Broken Detailed Balance and Entropy Production in the Human Brain. PNAS. 118/47, 2021. We cite this technical exercise by CCNY, Princeton, and University of Pennsylvania researchers including Danieile Bassett because it is able to connect our cerebral functions all the way to a complex physical basis. With this in place, a wider creative presence is noted across natural and societal phenomena. To perform biological functions, living systems must break detailed balance by consuming energy and producing entropy. At microscopic scales, broken detailed balance enables a suite of molecular and cellular functions, including computations, kinetic proofreading, sensing, adaptation, and transportation. But do macroscopic violations of detailed balance enable higher-order biological functions, such as cognition and movement? To answer this question, we adapt tools from nonequilibrium statistical mechanics to quantify broken detailed balance in complex living systems. (Significance) Lynn, Christopher, et al. Human Information Processing in Complex Networks. arXiv:1906.00926. University of Pennsylvania neuroengineers including Danielle Bassett contribute to the network revolution by showing how this connectomic feature serves our cognitive performance. See also A Mathematical Theory of Semantic Development in Deep Neural Networks by Andrew Saxe, et al (herein) for a similar concurrent study. Humans communicate using systems of interconnected stimuli or concepts from language and music to literature and science yet it remains unclear how the structure of these networks supports this process. Here we demonstrate that this perceived information depends on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (high entropy) and do so efficiently (low divergence from expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules. These results suggest that many real networks are constrained by the pressures of information transmission, and that they select for specific structural features. (Abstract excerpt) MacCormac, Earl and Maxim Stamenov, eds. Fractals of Brain, Fractals of Mind. Philadelphia: John Benjamin Publishing, 1996. How the sciences of complexity can reveal an intrinsic self-organization of brain development and behavior which takes on a fractal-like structure across many different spatial scales. Majhi, Soumen, et al. Chimera States in Neuronal Networks. Physics of Life Reviews. September, 2018. As complex network studies proceed apace, Indian Statistical Institute, Kolkata, and University of Maribor, Slovenia (Matjaz Perc) join a growing notice that brains seem to seek and reside at an optimum coexistence between a more or less orderly, conserve/create condition. Neuronal networks, similar to many other complex systems, self-organize into fascinating emergent states that are not only visually compelling, but also vital for the proper functioning of the brain. Recent research has shown that the coexistence of coherent and incoherent states, known as chimeras, is particularly important characteristic for neuronal systems. The emergence of this unique collective behavior is due to diverse factors that characterize neuronal dynamics and the functioning of the brain in general, including neural bumps and unihemispheric slow-wave sleep in some aquatic mammals. (Abstract excerpt) Mandelblit, Nili and Oron Zachar. The Notion of Dynamic Unit: Conceptual Developments in Cognitive Science. Cognitive Science. 22/2, 1998. The article describes a model akin to complex adaptive systems with applicability at every phase from physical substrates to neural processes, linguistics, and a collective social cognition. We suggest a common ground for alternative proposals in different domains of cognitive science which have previously seemed to have little in common. Our framework suggests a definition of unity which is based not on inherent properties of the elements constituting the unit, but rather on dynamic patterns of correlation across the elements. (229) Markman, Arthur and Eric Dietrich. Extending the Classical View of Representation. Trends in Cognitive Sciences. 4/12, 2000. An attempt to sort through several conflicting approaches to how the brain remembers and responds by considering theories of perceptual symbol systems, situated action, embodied cognition and dynamical systems. Martone, Maryann, et al. e-Neuroscience: Challenges and Triumphs in Integrating Distributed Data from Molecules to Brains. Nature Neuroscience. 7/5, 2004. From a complete issue on the subject, a review of how a collaborative field of neuroinformatics, similar to bioinformatics, is coming together to handle and integrate the vast amount of brain imaging and other neurological data pouring forth from laboratories worldwide. McClelland, James, et al. Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition. Trends in Cognitive Sciences. 14/8, 2010. Seven neuroscientists from Stanford, Princeton, University of California, Carnegie Mellon, University of Wisconsin and Indiana University (Linda Smith) contribute to the on-going reinvention of all things cerebral and clever in terms of nature’s complex systems. Of which the lead author and David Rumelhart were pioneers with 1980s parallel processing. See also McClelland’s “Emergence in Cognitive Science” in Topics in Cognitive Science (4/2, 2010) for an extensive acclaim of this property. In the same journal for May 2011, Danielle Bassett and Michael Gazzaniga offer “Understanding Complexity in the Human Brain” as a similar articulation. Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. (348) McNally, Luke, et al. Cooperation and the Evolution of Intelligence. Proceedings of the Royal Society B. Online April, 2012. With Sam Brown and Andrew Jackson, Trinity College Dublin, and University of Edinburgh, zoologists provide more credence for the “social brain” model, to wit if entities could ever stop fighting and actually help each other, it quite fosters learning activities, good for individuals and tribes to survive and thrive. The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the ‘social intelligence hypothesis’), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa. (Abstract)
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