VII. Our Earthuman Ascent: A Major Evolutionary Transition in Twindividuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

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.

Mathis, Mackenzie Weygandt et al. Decoding the brain: From neural representations to mechanistic models.. Cell. 187/21, 2024. In this late 2024 article, École Polytechnique Fédérale de Lausanne neuroscientists consider approaches that future studies could undertake with a decipherment and translation emphasis aided and enhanced by artificial intelligence facilities. Altogether in a philoSophian field view, a planetary sapiensphere version seems to just now be achieving her/his own retrospective description of the human cerebral faculties from which it arose. See also Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems by M. W. Mathis at arXiv:2411.15234, along with her Laboratory website and Wikipedia bio.

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas whose their function relies on distributed circuits to encode environmental and body-state features. In this perspective, we detail neural encodings and decodings and the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts can establish a foundational and translational science basis in motor, visual, and language processing. (Excerpt)

Furthermore, we recommend novel models that are mechanistic (biologically plausible) and statistical (can account for neural data) at the same time. Encoding-decoding models lie at the heart of this pursuit. Beyond neuroscience, advances in machine learning have led to a higher explanatory prowess grounded in iterative learning theory. Next, we illustrate this envisioned future via examples. (11)

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)

The finding that AG scales as a power law of AE means that gyrification is a property of a cortical surface that is self-similar down to a fundamental scale (the limit area between lissencephaly and gyrencephaly). This strongly suggests the existence of a single universal mechanism responsible for cortical folding (the alternative being some improbable multiscale fine-tuning) that over a range of scales generates self-similar, or fractal, surfaces. (76) The finding that cortical folding scales universally across clades, species, individuals, and parts of the same cortex implies that the single mechanism based on the physics of minimization of effective free energy of a growing surface subject to inhomogeneous bulk stresses applies across cortical development and evolution. (77)

Muller, Paul, et al. Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition.. PNAS. 122/14, 2025. Our work suggests critical dynamics to be the setpoint to measure optimal network function, thereby providing a unifying framework for heterogeneous mechanisms impacting cognition.In this paper, Charité—Universitätsmedizin, Berlin computational neurologists led by Christian Meisel provide a strong verification to date of a cerebral self-organized criticality which achieves an optimum performance and go on to show how its interruptions can result in neural maladies such as epilepsy.

Cognitive function emerges from cortical network dynamics and is often impaired in disorders like epilepsy. Physics and information theory suggest that brain networks operate optimally at a critical state between order and disorder. Using cognitive testing and intracranial EEG from persons with epilepsy (PwE), we find that proximity to critical dynamics predicts cognitive performance across multiple domains. We show that factors such as interictal epileptiform activity, antiseizure medications (ASMs), and sleep-like episodes perturb the critical state. Together, these findings support the concept of criticality as a prime way to understand cognition in neurological conditions. (Excerpt)

Munn, Brandon, et al. Multiscale organization of neuronal activity unifies scale-dependent theories of brain function. Cell. October 30, 2024. University of Sydney, University of Queensland and University of Newcastle, NSW neural imagers including Joseph Lizier report a latest integrative cerebral of local variances which then are seen to serve an integral cognitive coherence

Brain recordings collected at different resolutions support signatures of neural coding, which lead to scale-dependent theories. Here, we show that these results emerge from a hierarchal functional organization of neuronal activity observed across the whole brains of zebrafish and C. elegans as well as from Drosophila, mice, and macaques. Our findings suggest that this nonlinear organization is a universal principle conserved for its ability to adaptively link behavior to neural dynamics across multiple spatiotemporal scales while balancing functional resiliency and information processing efficiency. (Abstract excerpt)

[Prev Pages]   Previous   | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16  Next  [More Pages]