VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality
2. Systems Neuroscience: Multiplex Networks and Critical Function
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)
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)