(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Introduction
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Glossary
Recent Additions
Search
Submit

VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

2. Systems Neuroscience: Multiplex Networks and Critical Function

When this section to document 21st century advances about every aspect of our brain anatomy and cognitive performance was first posted in 2004, the fields of scale-free networks, self-organizing complexities, neuroimaging techniques, computational capacities, genetic influences and consciousness studies were at an early stage, just getting going. Circa 2108, neuroscientist researchers have now achieved a good grasp of how we think, learn, remember, speak, experience, feel, respond, cooperate and be creative. A brain’s development and active cognizance is seen to arise from a dynamic hierarchy of multiplex neural networks, modules, communities, hubs and linkages, poised in an optimum critical state. Around 2010 this sensory system was dubbed a “connectome,” akin to other -omic phases. Whole brain studies and integrations are moving toward global workspace, integrated information, and predictive expectation working model explanations.

To personalize the endeavor, among many contributors we note Giorgio Ascoli, Danielle Bassett, Gyorgy Buzsaki, Antonio Damasio, Steven Grossberg, Karl Friston, Michael Gazzaniga, Suzana Herculano-Houzel, Christoph Koch, Sebastian Seung, Hava Siegelmann, Olaf Sporns, and Giulio Tononi, everyone herein, and other sections throughout. Indeed, to fulfill this promise, national and international collaborative brain projects, akin to the human genome, are underway in the United States, Europe, Japan, and elsewhere. In regard, this neural network intricacy and acumen has become an iconic microcosm to an extent that, as Earthificial Intelligence notes, its deep learning abilities are used to analyze and discover from quantum to cultural realms. By this view, an AnthropoCosmo sapiensphere could even be seen to retrospectively quantify, and palliate, the myriad evolved personal organic brains from which it altogether fitfully arose.

Agnati, Luigi, et al. Mosaic, Self-Similarity Logic and Biological Attraction Principles. Communicative & Integrative Biology. 2/6, 2009. Senior scientists LA, University of Modena, Frantisek Baluska, University of Bonn, Peter Barlow, University of Bristol, and Diego Guidolin, University of Padova presciently discern an array of inherent structural features which take on a fractal as they recur at each and every minute neuronal to whole cerebral domains. Search each author for more contributions.

From a structural standpoint, living organisms are organized like a nest of Russian matryoshka dolls, in which structures are buried within one another. From a temporal view, this organisation is due to a history comprised of a set of time backcloths which have accompanied the passage of living matter from its origins up to the present day. The aim of the present paper is to indicate a possible course through time and suggest how today’s complexity has been reached by living organisms. This investigation will employ three conceptual tools, namely Mosaic, Self-Similarity Logic, and Biological Attraction principles.

Self-Similarity Logic indicates the self-consistency by which elements of a living system interact, irrespective of the spatio-temporal level. The term Mosaic indicates how, from a same set of elements assembled according to different patterns, it is possible to arrive at various constructions: hence, each system becomes endowed with different emergent properties. The Biological Attraction principle states that there is an inherent drive for association and merging of compatible elements at all levels of biological complexity. By analogy with the gravitation law in physics, biological attraction means that each living organism creates an attractive ‘field’ around itself. (Abstract excerpt)

Agnati, Luigi, et al. On the Nested Hierarchical Organization of CNS. Erdi, Peter, et al, eds. Computational Neuroscience: Cortical Dynamics. Berlin: Springer, 2004. A collaboration of ten authors from biochemical, neurological and medical sciences find the Central Nervous System to possess scalar realms from macromolecules to molecular networks, systems of these, local higher circuits, cellular networks, and their somatic systems. The same properties and dynamics occur at each stage which suggests a fractal self-similarity as its “animating principle.” In this view, our neurological soma appears as an iconic microcosm of how a genesis nature organizes itself and proceeds to knowing intelligence.

If we accept the view of the CNS as a nested hierarchical complex system, it is possible to search for schemes of functional organization at the various miniaturization levels. It is suggested that basically the same schemes for communication and elaboration of the information are in operation at the various miniaturization levels. This functional organization suggests a sort of “fractal structure” of the CNS. As a matter of fact, according to fractal geometry, fractal objects have the property that as we magnify them, more details appear but the shape of any magnified detail is basically the same as the shape of the original object. It is, therefore, suggested to introduce the term “fractal logic” to describe networks of networks where at the various levels of nested organization the same principles (logic) to perform operations are used. (29-30)

Agnati, Luigi, et al. One Century of Progress in Neuroscience Founded on Golgi and Cajal’s Outstanding Experimental and Theoretical Contributions. Brain Research Reviews. 55/1, 2007. A retrospective on the original Nobel prize insights of Santiago Ramon y Cajal and Camillo Golgi and an illustrated survey of the state of brain science today. A global theory of both form and function is at last possible via a nested hierarchy of fluid networks from the neurons that Ramon y Cajal first identified to the holistic cerebrum that Golgi advocated. And it ought to be noted that the worldwide computer web is taking on the same architecture and cogitation.

Agnati, Luigi, et al. The Brain as a “Hyper-Network:” The Key Role of Neural Networks as Main Producers of the Integrated Brain Actions via the “Broadcasted” Neuroconnectomics. Journal of Neural Transmission. 125/6, 2018. As global studies of dynamic multiplex structures gain a robust credence, University of Modena, University of Genova, National Institute of Drug Abuse, USA, and University of Padova (Diego Guidolin) neuroscientists can describe an iconic micro-universe which distinguishes our cerebral endowment and human acumen.

Investigations of integrative cerebral activities involve neural networks, glial, extracellular molecular, and fluid channels networks. Here we propose that this phenomena can result in a brain hyper-network that has as fundamental components known as tetra-partite synapses. Global signalling via astrocyte networks and pervasive signals, such as electromagnetic fields (EMFs), allow the integration of various networks at crucial nodes level, the tetra-partite synapses. The concept of broadcasted neuroconnectomics is introduced to describe highly pervasive signals involved in the information handling of brain networks at miniaturisation levels. Thus, it is surmised that neuronal networks are the “core components” of the brain hyper-network. (Abstract excerpt)

Allen, Micah and Karl Friston. From Cognitivism to Autopoiesis: Toward a Computational Framework for the Embodied Mind. Synthese. 195/6, 2018. University College London neuroscientists embellish their theories about our constant anticipatory perceptions by noting affinities with enactive embodiment and constructionist, self-making approaches (each malleable terms). These integrations allow prior representations, along with on-going experiential influences, to be accommodated. We surely live each day looking forward, but with reference to ingrained expectations. See also Friston’s publication page at the Wellcome Trust Centre for Neuroimaging for more contributions. On the arXiv eprint site can be found, for example, A Computational Hierarchy in the Human Cortex (1709.02323) and How Robust are Deep Neural Networks (1804.11313).

Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. Here, we present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. We go on to illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind. (Abstract edits)

The free energy principle tries to explain how (biological) systems maintain their order (non-equilibrium steady-state) by restricting themselves to a limited number of states. It says that biological systems minimise a free energy functional of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of variational free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception in neuroscience, where it is also known as active inference. (Wikipedia)

Almeida e Costa, Fernando and Luis Mateus Rocha. Introduction to the Special Issue: Embodied and Situated Cognition. Artificial Life. 11/1-2, 2005. Whose papers scope out a more realistic, animate context for Alife studies. An example is Smith and Gasser’s paper in the previous section.

The embodied cognition approach thus moved the modeling of intelligent systems from the study of intricate knowledge-based, representation-rich control systems to the study of the dynamics of networks of agent and environment components (self-organization). (6) In this alternative view, cognition is no longer modeled as the creation of agent-independent representations of the world, but as the embodied, evolving interaction of a self-organizing system with its environment. (6)

Altamura, Mario, et al. Toward Scale-Free Like Behavior under Increasing Cognitive Load. Complexity. Online June, 2012. University of Foggia, Italy, University of Tromso, Norway, Institute of Crystallography, CNR, Rome, and Deutsches Elektronen-Synchrotron DESY, Hamburg researchers find that a responsive, thoughtful human brain, as an archetypla nonlinear dynamical system, can be found to progressively move through phase transitions to emergent states of fractal criticality.

To understand how cognition and response selection processes might emerge from dynamic brain systems, we analyzed reaction times during the performance of both a working memory task and a choice reaction time task at different levels of “cognitive load.” Our findings suggest a continuous transition—tuned by load—from random behavior toward scale-free like behavior as an expanding connectivity process in a network poised near a critical point. (Abstract)

Anderson, James and Edward Rosenfeld, eds. Talking Nets. Cambridge: MIT Press, 1998. A series of interviews with the originators of neural network theory such as David Rumelhart, Teuvo Kokonen, and Stephen Grossberg.

I claim that, in order to self-organize intelligent adaptive processes in real time, the brain needs nonlinear feedback processes that describe dynamical interactions among huge numbers of units acting on multiple spatial and temporal scales. (Grossberg 195)

Arbib, Michael, et al. Neural Organization. Cambridge: MIT Press, 1998. A thorough text for both brain structure - “The Modular Architectonics Principle” and its self-organized function - “Neurodynamical System Theory.”

As we have seen, the brain is considered a prototype of hierarchical structures: Neural systems can be studied at one or more levels, such as the molecular, membrane, cellular, synaptic, network, and system levels….Both ontogenetic development and phylogenetic evolution are dynamic processes to be identified with self-organization phenomena. (4)

Ariswalla, Xerxes and Paul Verschure. The Global Dynamical Complexity of the Human Brain. Applied Network Science. Online December, 2016. (arXiv:1612.07106). University of Pompeu Fabra, Barcelona, cognitive informatics researchers at once recognize the growing value of the integrated information theory (Tononi, et al), which is in need of a further finesse (Oizumi). As the Abstract cites, this is achieved by insights into and avail of active network topologies. Each author’s publications page has more papers, for example see Connectomics to Semantomics (Arsiwalla herein).

How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. We compute the integrated information for both, the system's attractor states, as well as non-stationary dynamical states of the network. We then apply this formalism to brain networks to compute the integrated information for the human brain's connectome. (Abstract)

Arshavsky, Yuri. Role of Individual Neurons and Neural Networks in Cognitive Functioning of the Brain. Brain and Cognition. 46/414, 2001. An observation that discrete neurons are not wholly controlled by network dynamics but operate in a distinct “cell-autonomous” manner, a cerebral example of the generic particle/wave, agent/relation complementarity.

Ascoli, Giorgio. Trees of the Brain, Roots of the Mind. Cambridge: MIT Press, 2015. At the frontiers of natural philosophy, a George Mason University, Center for Neural Informatics, Structure and Plasticity neuroscientist surveys the 2010s era of sophisticated 3D cerebral imaging research. By virtue of these capabilities and their findings, a forest analogy is evoked for branching networks of local, arboreal neurons, synapses and axons whose intricate nestedness forms a whole brain anatomy. This vista of myriad nets within Nets leads to novel insights, as the quotes sample. Our neural connectome thus learns, thinks, and responds in a mindful way by necessarily comparing new experience with prior representations contained in the dynamic network tree topology. In a conclusion, this “brainforest” environment is seen as an apt microcosm for a similar encompassing network nature.

We have so far described how the third principle of the brain-mind relationship explains why appropriate knowledge of relevant background information gates the acquisition of new information by one-trial learning. The relationship between axonal-dendritic overlaps (corresponding to the potential to learn) and existing connectivity (representing knowledge) provides a direct neural mechanism for the familiar observation that experts can grasp new concepts in their discipline much faster than novices. Everyone finds it easier to learn new facts in their domains of expertise than in a completely novel field. (108)

Even our incomplete comprehension of neuronal structure and function has allowed us nonetheless to propose three core principles for linking the nervous system with the mind. We hypothesized in the first principle an identification of mental states and patterns of electric activity in the brain. A consequence of this equivalence is that knowledge, the ability to instantiate a given mental stare, is encoded in the connectivity of the network because electric activity in nervous systems flows through connections among neurons. This consideration led to formulation of a structural mechanism for what it means to acquire new knowledge, that is, to learn something. Specifically, in the second principle we equated learning to a change in network connectivity through the creation and removal of synapses. (181)

The third principle, which merely builds on the logic of the first two, is nevertheless the most radical and novel proposition of this book: that the spatial proximity of axons and dendrites, enabling synapse formation and elimination, corresponds to the capability for learning. Such a correspondence has far-reaching implications because it directly ties the branching structure of neurons to the fine line separating nurture from nature. Without the constraint of physical proximity between axons and dendrites, we could be able to learn anything from experience. With this rule in place, in constrast, each of us learns only those aspects of experience that are somehow compatible with our existing knowledge. (182)

Based on the principles exposed in this book, we can attempt to revisit the question of Reality. In their bare computational essence, brains can be viewed as gigantic networks whose sets of connections represent associations of observables learned through experience. We can thus offer a radical perspective of Reality. Reality constitutes an enormous interconnected web of co-occurring events. Each pair of events can be quantitatively expressed in the context of the entire web by the conditional probability representing the information content of their co-occurrence. Every human being (as well as, of course, all other animals and inanimate objects) is immersed in this universal web. From within, each person at any given moment witnesses a small fraction of co-occurring events based on his or her location, time, state of attention, and so forth. Brains evolved as networks (of neurons) themselves in order to represent most effectively the surrounding reality, thereby gaining predictive power and endowing their carriers with survival fitness. Such integration provides a natural conceptual framework to characterize the interactions among the world, the brain, and the mind. (211)

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]