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

2. Systems Neuroscience: Multiplex Networks and Critical Function

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)

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)

O’Brien, Gerard and Jonathan Opie. A Connectionist Theory of Phenomenal Experience. Behavioral and Brain Sciences. 22/1, 1999. Connectionism often connotes the apply of dynamical systems theory to cerebral activities.

Phenomenal experience consists of the explicit representation of information in neurally realized parallel distributed processing (PDP) networks. (127)

Pang, James, et al. Geometric constraints on human brain function. Nature. 618/566, 2023. By way of this work by seven Australian brain scientists we can notice a similar shift in cerebral studies to cellular versions from a prior century long focus on particulate neurons to a broad 2020s emphasis on ever dynamic multiplex linkages in between. See also Editorial: Is Now the Time for Foundational Theory of Brain Connectivity? by John Van Horn in Neuroinformatics (21:633. 2023) which cites the Pang paper

The anatomy of the brain constrains its function, but just how remains unclear. The prime paradigm is that neuronal dynamics are driven by interactions between discrete, functionally specialized cells. Neural field theory, a mathematical model for brain activity, suggests that structures represent a more fundamental constraint on dynamics. Here, we confirm these predictions by human MRI data whence the close link between geometry and function is explained by a wave-like dynamics. Our findings identify the role of topology in shaping function by a unified physical model. (excerpt)

Panzarasa, Pietro and Nicholas Jennings. Collective Cognition and Emergence in Multi-Agent Systems. Sun, Ron, ed. Cognition and Multi-Agent Interaction. Cambridge: Cambridge University Press, 2006. The reality and efficacy of a collaborative realm of human intelligence and cerebration is increasingly evident and lately gaining its theoretical ground. This further, “holistic,” self-organized phase is again the result of the universal system of local communication by discrete entities.

In the last few decades, the study of collective cognition has become an increasingly interdisciplinary area of research, weaving together an array of scientific contributions from a wide variety of scholarly fields including social psychology, organization science, complex adaptive systems, social network analysis, business studies, cognitive science, computer science and philosophy of mind. The fundamental idea underpinning most of these studies is that cognition is a social phenomenon that takes place and evolves in a reality jointly constructed by agents who interact within a network of social relations. To capture this idea, several “group mind”-like constructs have been introduced that extent to the group level a range of cognitive phenomena traditionally considered as belonging to the realm of the individual agent’s mind. (401)

Papadimitriou, Christos, et al. Brain Computation by Assemblies of Neurons. Proceedings of the National Academy of Sciences. 117/14464, 2020. Veteran Columbia University, Georgia Tech, and Graz University of Technology computer scientists propose and discuss ways how the content of neural associations might be projected and traced all the way to thoughtful linguistic results.

Our expanding understanding of the brain at the level of neurons and synapses, and the level of cognitive phenomena such as language, leaves a formidable gap between these two scales. Here we introduce a computational system which promises to bridge this gap: the Assembly Calculus. It encompasses operations on assemblies of neurons, such as project, associate, and merge, which appear to be implicated in cognitive phenomena, and can be shown, analytically as well as through simulations, to be plausibly realizable at the level of neurons and synapses. We demonstrate the reach of this system by proposing a brain architecture for syntactic processing in the production of language, compatible with recent experimental results. (Significance)

Papo, David, et al. Complex Network Theory and the Brain. Philosophical Transactions of the Royal Society B. 369/20130520, 2014. With neuroscientists Javier Buldu, Stefano Boccaletti, and Edward Bullmore, an Introduction to this topical issue intended to give neural self-organization phenomena a deeper basis in statistical and computational physics. For example see An Edge-Centric Perspective on the Human Connectome: Link Communities in the Brain, by Marcel de Reus, et al, Network-Guided Pattern Formation of Neural Dynamics by Marc-Thorsten Hutt, et al, and Function Brain Networks by David Papo, et al (search). In essence, a 21st century realization that micro cerebral connectome and macro emergent “cosmome” are truly one and the same.

Here we are focused on how a more formal, quantitative analysis of complex network organization could help us to understand the brain at micro and macro scales. Specifically, we are interested in the potential value added to neuroscience by the application of contemporary complex network theory: a statistical physics understanding of graph theory, itself a much older branch of pure mathematics. The statistical physics approach aims at explaining observable macroscopic behaviour of a given system as emerging in a non-trivial way from the interactions of a vast number of microscopic units or agents. Complex network theory can be thought of as a subfield of statistical physics for structurally disordered, dynamically heterogeneous systems with non-trivial topology; and as an extension of graph theory to systems with high structural heterogeneity and inherently dynamical properties, two key properties of the vast majority of real-life systems, including brains. (2)

The abstraction of graphs from the details of the underlying data means that the same mathematical language can be used to quantify topological properties at micro and macro scales, to link the organization of anatomical and functional networks, to compare the topology of brain networks across species, and to consider the topology of brain networks in general compared with other complex systems, including non-biological networks. This in turn has encouraged a shift in perspective towards fractal, scale-invariant or indeed universal properties of brain networks that complement the traditional focus on the unique and species-specific anatomical details of their organization. (2)

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