VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
2. Systems Neuroscience: Multiplex Networks and Critical Function
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)
Meuneir, David, et al. Modular and Hierarchically Modular Organization of Brain Networks. Frontiers in Neuroscience. 4/Article 200, 2010. With co-authors Renaud Lambiotte and Edward Bullmore, University Of Cambridge, and Imperial College researchers provide more evidence of this archetypal architecture and performance of our cerebral embrainment. Are we people even more of a microcosmic epitome then ever imagined, so that human and universe are truly a luminous mirror to each other?
Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. (1)
Meunier, David, et al. Hierarchical Modularity in Human Brain Functional Networks. Frontiers in Neuroinformatics. Vol. 3, Art. 37, 2009. From this online journal, a posting that the “architecture of complexity” proposed some 50 years ago by Herbert Simon of recurrent modular wholes within wholes which shape and sustain a viable emergence, can similarly distinguish our cerebral faculties. These latest insights serve to identify the brain’s nested networks of intercommunicating nodes and links as a further universal feature.
The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Moon, Joon-Young, et al. General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks. PLoS Computational Biology. Online April, 2015. We cite this “connectome project” by University of Michigan Medical School, Center for Consciousness Science researchers, including George Mashour, as another example of insights into dynamic, reciprocal interactions between the integral cerebrum and individual neurons.
The balance of global integration and functional specialization is a critical feature of efficient brain networks, but the relationship of global topology, local node dynamics and information flow across networks has yet to be identified. One critical step in elucidating this relationship is the identification of governing principles underlying the directionality of interactions between nodes. Here, we demonstrate such principles through analytical solutions based on the phase lead/lag relationships of general oscillator models in networks. We confirm analytical results with computational simulations using general model networks and anatomical brain networks, as well as high-density electroencephalography collected from humans in the conscious and anesthetized states. (Abstract)
Mota, Bruno and Suzana Herculano-Houzel. Cortical Folding Scales Universally with Surface Area and Thickness, not Number of Neurons. Science. 349/74, 2015. As the quotes convey, Federal University of Rio de Janeiro neuroscientists contribute to current affirmations of nature’s repetitive self-similarity that occurs as archetypal exemplars in our own brain anatomy, physiology and cognizance. A review in the same issue, Knowing When to Fold Them by Georg Striedter and Shyam Srinivasan, extols the quality of this work. See also Cortical Folding by Streidter, et al in the Annual Review of Neuroscience (38/291, 2015).
Larger brains tend to have more folded cortices, but what makes the cortex fold has remained unknown. We show that the degree of cortical folding scales uniformly across lissencephalic and gyrencephalic species, across individuals, and within individual cortices as a function of the product of cortical surface area and the square root of cortical thickness. This relation is derived from the minimization of the effective free energy associated with cortical shape according to a simple physical model, based on known mechanisms of axonal elongation. This model also explains the scaling of the folding index of crumpled paper balls. We discuss the implications of this finding for the evolutionary and developmental origin of folding, including the newfound continuum between lissencephaly and gyrencephaly, and for pathologies such as human lissencephaly. (Abstract)
Mustafa, Nazahah, et al. Brain Structural Complexity and Life Course Cognitive Change. NeuroImage. 61/694, 2012. In one of the first studies of its kind, Malaysian and Scottish neuroscientists employ MRI analysis of neural anatomies from infants to seniors to find a consistent degrees of fractal self-similarity with mental health and acuity. Indeed, children are on a sharp up swing while for folks in their seventies the quality declines. Compare with work by Andrew Seely, et al in Canada who find the same non-Euclidean topologies to characterize the shape of central nervous systems. In both cases, this feature is said to provide a novel way to assess well-being or lack thereof. And per the quote, this description of cerebral structure could readily be taken to describe universal nature everywhere – human microcosm and universe macrocosm as true correspondents.
The cerebral cortex is a fractal structure made up of parts that are in some ways similar to the whole. The cortical fractal structure can be characterised by a single numerical value (the fractal dimension, FD) that summarises the irregularity of the external cortical surface and the boundary between subcortical grey and white matter. Development and ageing of the human brain can be studied with FD and have shown increasing cortical complexity from early foetal life through childhood and into adulthood until decreasing complexity is seen in late life and in Alzheimer's disease. (694)
Nicolelis, Miguel and Sidarta Ribeiro. Seeking the Neural Code. Scientific American. December, 2006. Presently at Duke University, the authors are co-founders of the new International Institute of Neuroscience of Natal, Brazil. A good survey by prolific researchers on the transition from an emphasis in this field on point neurons to their contextual place and role in avalanches of pulsing electrical networks.
Nolfi, Stefano, et al. Behavior and Mind as a Complex Adaptive System. Adaptive Behavior. 16/2-3, 2008. An introduction to a special issue on such perceptions of a nested cerebral and cognitive self-organization. See also online a survey article on this approach by Nolfi in Complexus (2/3-4, 2005). One more take that human and universe embody, embrain and dynamically mirror each other.
Nunez, Paul and Ramesh Srinivasan. Hearts Don’t Love and Brains Don’t Pump: Neocortical Dynamic Correlates of Conscious Experience. Journal of Consciousness Studies. 14/8, 2007. Neuroscientists from Tulane University and UC Irvine illustrate their views on how the brain functions by way of a grand analogy. The same self-organizing, synergetic, hierarchical complexity is said to characterize both neural and social systems. Just as the brain is dynamically arrayed from neuron to neocortex, so civilizations are from individuals to global populations. Cerebral consciousness arises from resonantly tuned networks and synaptic fields. The article doesn’t go there, but it implies that our ultra-intricate global society, with a similar number of people as the brain has neurons, as Peter Russell has noted, ought to possess its own cognitive capabilities and knowledge, which is the basic premise of this website.
For genuine scientific reasons that are independent of our sociological metaphor, it appears that two general features of brain tissue are especially important in healthy brains, hierarchical interactions and non-local interactions. These properties are also important characteristics of the human global social system. The cooperation and conflict between individuals, cities, nations and so forth serves as a convenient metaphor for neural interactions at multiple scales. (22)