VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
2. Systems Neuroscience: Multiplex Networks and Critical Function
Betzel, Richard, et al. Multi-Scale Community Organization of the Human Structural Connectome and its Relationship with Resting-State Functional Connectivity. Network Science. 1/3, 2013. As an ascendant planetary progeny proceeds to quantify, explain and discover a genesis universe, in this new Cambridge online journal, a team from Indiana University and the University of Lausanne, including Olaf Sporns, offer a capsule of current progress. The paper opens by citing how a decade of studies of natural network phenomena have led to the distillation of an independent, universally applicable, description. In regard, human brains, via intricate, scintillating connections, can be seen as a premier exemplar. So we peoples do indeed possess an archetypal microcosm of this macro cosmome. Compare with Tognoli, Tononi, Rubinov, many other entries as they verge on a grand synthesis.
The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. In this report, we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scale. (Abstract)
Bota, Mihail, et al. From Gene Networks to Brain Networks. Nature Neuroscience. 6/8, 2003. A proposal to apply similar techniques used in the study of the genome to comprehend neural processes. A prototype Brain Architecture Knowledge Management System is described akin to bioinformatics to help sort out nomenclature issues and to express their dynamic interconnections.
Botvinick, Matthew. Hierarchical Models of Behavior and Prefrontal Function. Trends in Cognitive Sciences. 12/5, 2007. The Princeton psychologist in this article, and others accessible from his website, professes an isomorphic alignment between behavioral scales and their neural representation as mind and active manifestation come to mirror each other.
Breakspear, Michael and Cornelis Stam. Dynamics of a Neural System with a Multiscale Architecture. Philosophical Transactions of the Royal Society B. 360/1051, 2005. Based on extensive neuroimaging studies of cerebral connectivity, the human brain is found to be arranged as a nest of self-similar modules and interactive networks. This composition is said to reflect nature’s newly discovered universal scaling laws. By these advances, one can add that human brains appear to exhibit the same pattern and process as life’s evolutionary genesis and the organic universe from which they emerge. A significant paper.
The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales – neurons, minicolumns, cortical columns, functional brain regions, and so on. (1051) A multiscale hierarchy of such neuronal ensembles – with interdependences between scales – is then constructed using the wavelet transform…this yields a hierarchy of neuronal systems whose spatial scales are geometrically nested… (1052)
Buckner, Randy and Fenna Krienen. The Evolution of Distributed Association Networks in the Human Brain. Trends in Cognitive Sciences. 17/12, 2013. In work noted by the New York Times as “In the Human Brain, Size Really Isn’t Everything” (December 26, 2013), Harvard University and Massachusetts General Hospital neuroscientists propose that as earlier, rudimentary neural architectures became “untethered,” this allowed more complex connective topologies to form in primate and homo sapiens brains. A volumetric increase is not enough, an increasingly intricate “connectome” makes the difference. And of further importance, as per the quote, neural evolution is seen in an embryonic way as a expansive deployment from an original archetypal geometry.
The inherited constraints of development and the general plan, or Bauplan, of the brain are powerful limiters on how neural circuits can evolve across generations. Here we raise the possibility that critical features of the association cortex, linked to size scaling, may contribute to the human brain’s extraordinary capabilities. The central idea is that a distributed form of circuit may have become increasingly prominent when ancient rules of development were expressed in an expanding cortical mantle. The possibility that simple mechanisms play a major role in recent brain evolution is comforting because it demystifies the gap between our brain’s capabilities and those of our ancestors. (661-662)
Bullmore, Edward, et al. Generic Aspects of Complexity in Brain Imaging Data and other Biological Systems. Neuroimage. 47/3, 2009. University of Cambridge, NIH, and University of Melbourne neuroscientists, as the extended quotes aver, contend that after a decade of intensifying progress in endeavors to view neural anatomy and activity as an iterative, emergent self-organization, as every other domain of life and society has done also, that a universality of such similar kind can be admitted and appreciated. Upon reflection, this is huge for we are now invited to realize in our midst an epochal worldwide discovery of an untangled, comprehensible nature, arising from and manifestly representing a true universe to human genesis.
A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. (Abstract, 1125)
Buzsaki, Gyorgy. Neural Syntax: Cell Assemblies, Synapsembles, and Readers. Neuron. 68/3, 2011. Natural realms from quanta and genomes to cooperative groups are presently being reinterpreted and better understood not only by complex systems science, but by way of novel appreciations of their linguistic, semiotic essence. As the quotes note, the Rutgers University behavioral neuroscientist (search) proceeds with a similar approach to parse cerebral structure and dynamics in such grammatical terms. See also Buzsaki's 2006 book Rythmns of the Brain for more. And what might we take from this – is a greater creation being found that is, as our traditions know, deeply textual, poetic, even scriptural, in kind?
A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as ‘‘cell assemblies,’’ underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by ‘‘reader-actuator’’ mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights (‘‘synapsembles’’). (Abstract, 362)
Buzsaki, Gyorgy. Rhythms of the Brain. Oxford: Oxford University Press, 2006. A Hungarian-American professor of Molecular and Behavioral Neuroscience at Rutgers University informs and enriches this endeavor by the principles of nonlinear science, via theory and experiment, to articulate an innately self-organizing cerebral formation and activity. By this vista, our brains, distinguished by a universal pattern and process across nested scales, can be appreciated to embody nature’s independent while emergent dynamics. This intricate volume, graced not by chapters but “cycles,” joins human and universe, and goes on to suggest a rudimentary global brain may be likewise generating itself. But a disclaimer is added on page 4 that Nature, of course, has no laws, desires, goals, or drives. (Is this necessary for publication or membership?) But Buzsaki and colleagues make a major contribution to a genesis vision, which is worth extended excerpts.
Oftentimes, not only does complexity characterize the system as a whole, but also its constituents (e.g. neurons) are complex adaptive systems themselves, forming hierarchies at multiple levels. All these features are present in the brain’s dynamics because the brain is also a complex system. (11) The scale invariance of fractals implies that knowledge of the properties of a model system at any scale can be used to predict the structure of the real system at larger or smaller scales. Applying this knowledge to neuroscience, knowing the fundamental properties of the organization of the cerebral cortex in any mammalian species and the rules of network growth, the principal structural organization of smaller and larger brains can be predicted. (30)
Buzsaki, Gyorgy, et al. Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms. Neuron. 80/3, 2013. In a “Neuroscience Retrospective” issue, Buzsaki, NYU, Nikos Logothetis, MPI Biological Cybernetics, and Wolf Singer, MPI Brain Research, contribute a current summary and bibliography for this reconception, as many other fields, by way of nonlinear complexities. By these theories, a mature synthesis and verification has been reached of a unified cerebral architecture and activity graced by nested, self-organized critical dynamics. Please search Buzsaki and Singer for prior papers.
Despite the several-thousand-fold increase of brain volume during the course of mammalian evolution, the hierarchy of brain oscillations remains remarkably preserved, allowing for multiple-time-scale communication within and across neuronal networks at approximately the same speed, irrespective of brain size. Deployment of large-diameter axons of long-range neurons could be a key factor in the preserved time management in growing brains. We discuss the consequences of such preserved network constellation in mental disease, drug discovery, and interventional therapies. (Abstract)
Cabessa, Jeremie and Hava Siegelmann. The Computation Power of Interactive Recurrent Neural Networks. Neural Computation. 24/4, 2012. University of Massachusetts, Amherst, computational neuroscientists take these cerebral complexities to exemplify how nature evolves, develops and learns. We are then invited to realize that the same dynamical trial and error, feedback to move forward, iterative process is in effect everywhere. See also Turing on Super-Turing and Adaptivity by Hava Siegelmann in Progress in Biophysics and Molecular Biology (113/117, 2013), and search Richard Watson 2014 herein.
In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities. (Abstract)
Carruthers, Peter. Practical Reasoning in a Modular Mind. Mind & Language. 19/3, 2004. (As an initial note, it appears that “modular” schools also exist in cognitive science with various persuasions and viewpoints.) Philosopher Carruthers makes a case for domain-specific modules in the brain which arose in evolution in response to changing environments. In support of an evolutionary psychology, they are now seen to influence the mores of human behavior.
Carruthers, Peter. The Architecture of the Mind. Oxford: Clarendon Press, 2006. The University of Maryland philosopher makes a strong case for a massively modular brain, with certain evolutionary roots, whose remnants are with us today. In this regard, a broadly conceived evolutionary psychology is endorsed. But this academic endeavor in so many books and journals seems to labor within an assumed mechanical paradigm tacitly devoid of any extant identity or purpose. That minds are modular because they spring from and exemplify a universal tendency of self-organizing systems from genes to galaxies to form modules is not appreciated.