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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Thiebaut de Schotten, Michel and Stephanie Forkel. Thiebaut de Schotten, Michel and Stephanie Forkel. The emergent properties of the connected brain. Science. 378/505, 2022. University of Bordeaux and Sorbonne University neuroscientists extol frontier neuroimaging techniques for their potential to discern and elucidate cerebral complexities in health and sickness. See also The complex brain: connectivity, dynamics, information by Olaf Sporns in Trends in Cognitive Sciences (26/12, 2022).

• There is more to brain connections than the mere transfer of signals between brain regions. Behavior and cognition emerge through cortical area interaction which requires integration between local and distant areas orchestrated by dense networks. The imaging of connections has identified the driving factors behind the neurobiology of cognition. Differences between species and among humans then further the understanding of brain evolution. The prediction of long-term symptoms is now preferentially based on brain disconnections. This paradigm shift will reshape our brain maps and challenge current brain models. (Abstract)

Thompson, Evan and Francisco Varela. Radical Embodiment: Neural Dynamics and Consciousness. Cognitive Sciences. 5/10, 2001. One of Francisco Varela’s last contributions which finds sentient awareness to be rooted in an “enactive” brain-body-world interplay rather than confined to purely neuronal events.

Tognoli, Emmanuelle and Scott Kelso. The Metastable Brain. Neuron. 81/1, 2014. The Florida Atlantic University, Human Brain and Behavior Laboratory, neurosciencists provide a summary update of their insights into our dynamic cerebral reciprocities of autonomous neurons and modular syntheses. A 2014 book by this title is in the works, search also for Kelso, et al, 2009 and 2013.

Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain’s dynamic cognitive, behavioral, and social functions. (Abstract)

Treffner, Paul and Scott Kelso. Dynamic Encounters: Long Memory During Functional Stabilization. Ecological Psychology. 11/2, 1999. Human and universe share the same creative agency.

Evidence and theory suggest that the coordination of human perception and action may be understood as a self-organizing complex system that exhibits great flexibility by operating nearby critical points of instability. (103)

Tsuda, Ichiro. Toward an Interpretation of Dynamic Neural Activity in Terms of Chaotic Dynamical Systems. Behavioral and Brain Sciences. 24/6, 2001. The conventional view emphasizes static elements while new insights focus on the fluid, shifting relations between modular components. The active brain is then seen to self-organize by the interplay of retained representation and creative perception.

According to this point of view, a single neuron or neuron assembly is represented by a single code and also by a multiple code; the information representation is realized both by the state of neurons and by the dynamic relation among states. (793)

Van Gelder, Tim. The Dynamical Hypothesis in Cognitive Science. Behavioral and Brain Sciences. 21/5, 1998. An affirmation of an integrative, self-organizing mental activity to supplant the prior digital computational model.

Van Orden, Guy. Nonlinear Dynamics and Psycholinguistics. Ecological Psychology. 14/1-2, 2002. An introduction to a special issue on this topic. While 20th century cognitive psychology was founded on reductionism and linearity, this article recognizes the irreducible, reciprocal relations between agents and environments. These take on the characteristic form of fractally nested self-organizing systems.

Van Pelt, J., et al, eds. The Self-Organizing Brain: From Growth Cones to Functional Networks. Amsterdam: Elsevier, 1994. Proceedings of the 18th International Summer School of Brain Research, University of Amsterdam, August 1993. How the sciences of complexity are bringing a novel understanding of mutually interrelated brain structure and function. Neuronal self-organization is seen to have an epigenetic character beyond molecular programs so as to remove a genetic determination.

Varela, Francisco, et al. The Embodied Mind. Cambridge: MIT Press, 1991. An extraordinary work of bridge building from the expansive self of Madhyamika Buddhist psychology over a computational neuroscience view of a fragmented self toward a novel “enactive” theory drawing on connectionist, self-organizing networks.

Vazquez-Rodriguez, Bertha, et al. Stochastic Resonance at Criticality in a Network Model of the Human Cortex. Nature Scientific Reports. 7/13020, 2017. Universidad Nacional Autónoma de México, Indiana University, and Lausanne University Hospital neuroscientists including Olaf Sporns add significant neuroimage evidence for an innate propensity of human brains to situate cognitive activities in an optimal, critically poised state between noise and content. By so doing, our neural acuity becomes an ultra-complex exemplar of a uniVerse to human evolutionary genesis. See also The Signatures of Conscious Access and its Phenomenology are Consistent with Large-Scale Communication at Criticality by Enzo Tagliazucchi in Consciousness and Cognition (55/136, 2017).

Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain (connectome). The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality. (Abstract)

Growing evidence supports the hypothesis that the dynamics of the brain resembles the dynamics of a system near a critical point. This suggests that many functionally important features of brain dynamics may be optimized at criticality. Recent work has shown that a discrete state dynamical model implemented on a network of neuroanatomical connections exhibits a phase transition similar to that observed in a percolation model, where the average size of the second biggest cluster of active nodes reaches its maximum value for a
specific activation threshold. (1) In this work we determine quantitatively the amount of noise required for the best transmission of signals through the structural network of the brain’s connectome, and its relationship with the hypothesis of the brain operating near criticality. (2)

Vernon, David. Artificial Cognitive Systems. Cambridge: MIT Press, 2014. The University of Skovde, Sweden, professor of informatics provides a good update review and synthesis of the field and frontier of Cognitive Science. In this nascent paradigm, a cerebral, and robotic, cognizance is seen as emergent in some self-constructive way. The view can then join connectionist, dynamic systems theory, and enactive approaches, each of which is well explained. A “radical constructionism” is noted which does not deny prior representations but holds that an agent or entity proceeds to adapt and compose their own viable reality. If we might shift from microcosm to macrocosm, could we imagine a radical self-realizing uniVerse, of which peoples are the selves meant to achieve this?

Walter, Nike and Thilo Hinterberge. Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features.. Neuroscience of Consciousness. Volume 1, 2022. University Hospital of Regensburg, Germany systems psychologists advantage the latest neuro-instrument and computational analyses to achieve deeper explanations and proofs of this optimum dynamic poise. Once again, nature’s preferred avail of this best balance is found in how we experience, think, learn and respond.

, This study was based on recent findings that distinct states of consciousness are quantifiable by neural complexities and critical dynamics. To test this, we compared the electrophysiological correlates of meditation states using nonlinear techniques. We used analytical methods from criticality theory (detrended fluctuation analysis, neuronal avalanche), complexity measures (multiscale entropy, Higuchi’s fractal dimension), and power spectral density. The meditation states could be quantified with nonlinear measures by the degree of neuronal complexity, long-range temporal correlations, and power law avalanches. (Excerpt)

In recent years, the hypothesis arose that neural dynamics reside in a self-organized criticality. This premise is based on theoretical and experimental work in physics, which show that the multiscale dynamics of a complex system are distinguished by branching avalanches. These statistics reveal whether the system is in a critical state at the edge between order and disorder. Critical state dynamics as these were associated with optimized network functions of information processing such as input susceptibility, maximized dynamic range, storage capacity as well as computational power. (1)

In conclusion, electrophysiological differences of distinct meditation states were identified and the relationship between non-linear complexity, critical brain dynamics, and spectral features was determined. The meditation states could be discriminated with nonlinear measures and quantified by the degree of neuronal complexity, LRTC, and power law distributions in neuronal avalanches. (8)

Neuroscience of Consciousness is an open access journal which publishes papers on the biological basis of consciousness. We welcome contributions from neuroscience, cognitive science, psychology, philosophy, computer science, and allied disciplines.. As well as the primary phenomenon of consciousness, relevant topics include interactions between conscious and unconscious processes; selfhood; emotion; metacognition and higher-order consciousness; intention, volition, and agency; individual differences in consciousness; altered states of consciousness; and consciousness in infants, non-human animals, and machines.

[Prev Pages]   Previous   | 11 | 12 | 13 | 14 | 15 | 16 | 17  Next