VII. WumanKinder: An EarthSphere Transition in Individuality
2. Systems Neuroscience: Multiplex Networks and Critical Function
Pospelov, Nikita, et al. Spectral Peculiarity and Criticality of a Human Connectome. Physics of Life Reviews. Online June 16, 2019. Six Russian neurotheorists based at Lomonosov Moscow State University describe novel techniques and insights which adds more evidence that our hyperactive brains are truly situated at an optimum critically poised state.
We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. We found that the spectral density of adjacency matrices of human connectome has maximal deviation from randomized networks, compared to other organisms. We discovered that for macaque and human connectomes the conservation of local clusterization is crucial, while for primitive organisms the conservation of averaged clusterization is sufficient. We found that the level spacing distribution of the spectrum of human connectome Laplacian matrix corresponds to the critical regime. This observation provides strong support for debated statement of the brain criticality. (Abstract)
Pribram, Karl and Joseph King, eds. Learning as Self-Organization. Mahwah, NJ: Erlbaum, 1996. A collection of innovative papers from both scientific and traditional (Asian) perspectives on the revolution in neuroscience due to nonlinear theories.
Priesemann, Viola, et al. Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans. PLoS Computational Biology. 9/3, 2013. Priesemann, MPI Brain Research, with Mario Valderrama, University of Los Andes, Michael Wibral, Goethe University, and Michel Le Van Quyen, CRICM, Paris, achieve a robust affirmation that cerebral faculties employ an optimal state of critical, self-organized poise. Search Wibral, et al for a 2014 paper with Priesemann for further frontiers of computational, SOC neural net research.
Neuronal activity differs between wakefulness and sleep states. In contrast, an attractor state, called self-organized critical (SOC), was proposed to govern brain dynamics because it allows for optimal information coding. But is the human brain SOC for each vigilance state despite the variations in neuronal dynamics? We characterized neuronal avalanches – spatiotemporal waves of enhanced activity - from dense intracranial depth recordings in humans. We showed that avalanche distributions closely follow a power law – the hallmark feature of SOC - for each vigilance state. However, avalanches clearly differ with vigilance states: slow wave sleep (SWS) shows large avalanches, wakefulness intermediate, and rapid eye movement (REM) sleep small ones. Our SOC model, together with the data, suggested first that the differences are mediated by global but tiny changes in synaptic strength, and second, that the changes with vigilance states reflect small deviations from criticality to the subcritical regime, implying that the human brain does not operate at criticality proper but close to SOC. Independent of criticality, the analysis confirms that SWS shows increased correlations between cortical areas, and reveals that REM sleep shows more fragmented cortical dynamics. (Abstract)
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)
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)
Genes, Brain, and Cognition.
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.
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.
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)
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)
Saxe, Andrew, et al.
A Mathematical Theory of Semantic Development in Deep Neural Networks.
Proceedings of the National Academy of Sciences..
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)