447:. Metastability is basically a theory of how global integrative and local segregative tendencies coexist in the brain. The Operational Architectonics is centered on the fact that in the metastable regime of brain functioning, the individual parts of the brain exhibit tendencies to function autonomously at the same time as they exhibit tendencies for coordinated activity. In accordance with Operational Architectonics, the synchronized operations produced by distributed neuronal assemblies constitute the metastable spatial-temporal patterns. They are metastable because intrinsic differences in the activity between neuronal assemblies are sufficiently large that they each do their own job (operation), while still retaining a tendency to be coordinated together in order to realize the complex brain operation.
197:
209:
together and move away to and from the midline of the body. To illustrate coordination dynamics, the subjects were asked to move their fingers out of phase with increasing speed until their fingers were moving as fast as possible. As movement approached its critical speed, the subjects’ fingers were found to move from out-of-phase (windshield-wiper-like) movement to in-phase (toward midline movement).
309:
occurred. After a short period, the movements of the two subjects sometimes became coordinated and synchronized (but other times continued to be asynchronous). The link between EEG and conscious social interaction is described as Phi, one of several brain rhythms operating in the 10 Hz range. Phi consists of two components: one to favor solitary behavior and another to favor interactive (
397:
One interesting aspect of the GNW is that with sufficient intensity and length over which a signal travels, a small initiation signal can be compounded to activate an "ignition" of a critical spike-inducing state. This idea is analogous to a skier on the slope of a mountain, who, by disrupting a few
433:
tasks shows a much wider use of integrated portions of the brain than in identical unconscious input. The wide distribution and constant signal transfer between different areas of the brain in experimental results is a common method to attempt to prove the neural workspace hypothesis. More studies
308:
to provide experimental results for the theory of social coordination dynamics. In Kelso's experiments, two subjects were separated by an opaque barrier and asked to wag their fingers; then the barrier was removed and the subjects were instructed to continue to wag their fingers as if no change had
39:
Metastability, a state in which signals (such as oscillatory waves) fall outside their natural equilibrium state but persist for an extended period of time, is a principle that describes the brain's ability to make sense out of seemingly random environmental cues. In the past 25 years, interest in
180:
model, which represents networks composed of integrated neural systems communicating with one another between unstable and stable phases, has become an increasingly popular theory underpinning the understanding of metastability. Coordination dynamics forms the basis for this dynamical system model
393:
The global workspace hypothesis is another theory to elucidate metastability, and has existed in some form since 1983. This hypothesis also focuses on the phenomenon of re-entry, the ability of a routine or process to be used by multiple parts of the brain simultaneously. Both the DCH and global
204:
In the last 20 years, the HKB model has become a widely accepted theory to explain the coordinated movements and behaviors of individual neurons into large, end-to-end neural networks. Originally the model described a system in which spontaneous transitions observed in finger movements could be
208:
In the mid-1980s HKB model experiments, subjects were asked to wave one finger on each hand in two modes of direction: first, known as out of phase, both fingers moving in the same direction back and forth (as windshield wipers might move); and second, known as in-phase, where both fingers come
353:
interact in the form of synchronous oscillation. The interaction between distinct neuronal groups forms the dynamic core and may help explain the nature of conscious experience. A critical feature of the DCH is that instead of thinking binarily about transitions between neural integration and
162:
regime, the brain is in the critical state necessary for a conscious response to weak or chaotic environmental signals because it can shift the random signals into identifiable and predictable oscillatory waveforms. While often transient, these waveforms exist in a stable form long enough to
275:
expand upon the patterns seen in EEG by providing visual confirmation of coordinated dynamics. The MEG, which provides an improvement over EEG in spatiotemporal characterization, allows researchers to stimulate certain parts of the brain with environmental cues and observe the response in a
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activity is a prominent example of the brain's ability to be modeled dynamically and is a common example of coordination dynamics. Continuous study of these and other oscillations has led to an important conclusion: analyzing waves as having a common signal phase but a different
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In addition to study investigating the effects of metastable interactions on traditional social function, much research will likely focus on determining the role of the coordinated dynamic system and the global workspace in the progression of debilitating diseases such as
296:
following certain patterns of interaction. This work is aimed at understanding how human social interaction is mediated by metastability of neural networks. fMRI and EEG are particularly useful in mapping thalamocortical response to social cues in experimental studies.
442:
Although the concept of metastability has been around in
Neuroscience for some time, the specific interpretation of metastability in the context of brain operations of different complexity has been developed by Andrew and Alexander Fingelkurts within their model of
55:
measures the gross electrical activity of the brain that can be observed on the surface of the skull. In the metastability theory, EEG outputs produce oscillations that can be described as having identifiable patterns that correlate with each other at certain
394:
neuronal workspace (GNW) models involve re-entrance, but the GNW model elaborates on re-entrant connectivity between distant parts of the brain and long-range signal flow. Workspace neurons are similar anatomically but separated spatially from each other.
253:. The latter idiosyncrasy has served as the basis for assuming an interaction and transition between neural subsystems. Analysis of activation and deactivation of regions of the cortex has shown a dynamic shift between dependence and
212:
The HKB model, which has also been elucidated by several complex mathematical descriptors, is still a relatively simple but powerful way to describe seemingly-independent systems that come to reach synchrony just before a state of
370:(a phenomenon describing the overall reciprocity and interactivity between signals in distant parts of the brain through coupled signal latency). Neuronal selectivity involves mechanochemical events that take place pre- and post-
225:
In the last 10 years, the HKB model has been reconciled with advanced mathematical models and supercomputer-based computation to link rudimentary coordination dynamics to higher-order processes such as learning and memory.
80:
are processing in parallel and which neuronal networks are intertwined. In many cases, metastability describes instances in which distal parts of the brain interact with each other to respond to environmental stimuli.
421:) and then asking the subject to identify the forward "translation" of these words. Not only did fMRI detect activity in the word-recognition portion of the cortex, but additionally, activity is often detected in the
72:
waveform. When neurons are integrated into the neural network by interfacing neurons with each other, the dynamical oscillations created by each neuron can be combined to form highly predictable EEG oscillations.
1111:(1991) Behavioral and neural pattern generation: the concept of neurobehavioral dynamical system (NBDS). In: Koepchen HP (ed) Cardiorespiratory and motor coordination.Springer, Berlin Heidelberg New York.
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The so-named HKB model is one of the earliest and well-respected theories to describe coordination dynamics in the brain. In this model, the formation of neural networks can be partly described as
374:
whereby neuronal connections are influenced by environmental experiences. The modification of synaptic signals as it relates to the dynamic core provides further explanation for the DCH.
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designed to graph neuronal signals as chaotic and non-linear has provided some algorithmic basis for analyzing how chaotic environmental signals are coupled to enhance selectivity of
377:
Despite growing evidence for the DCH, the ability to generate mathematical constructs to model and predict dynamic core behavior has been slow to progress. Continued development of
284:, allowing for a virtually real-time investigation of the active turning-on and -off of selected parts of the brain in response to environmental cues and conscious tasks.
193:, where individual neurons and small neuronal systems aggregate and coordinate to either adapt or respond to local stimuli or to divide labor and specialize in function.
354:
non-integration (i.e., that the two are either one or the other with no in-between), the metastable nature of the dynamic core can allow for a continuum of integration.
345:. The dynamic core hypothesis (DCH) reflects the use and disuse of interconnected neuronal networks during stimulation of this region. A computer model of 65,000
292:
A developing field in coordination dynamics involves the theory of social coordination, which attempts to relate the DC to normal human development of complex
245:
Some unusual characteristics of these waves: they are virtually simultaneous and have a very short onset latency, which implies that they operate faster than
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Kaplan AYa (1998) Nonstationary EEG: methodological and experimental analysis. Usp Fiziol Nauk (Success in
Physiological Sciences) 29:35–55 (in Russian).
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Fingelkurts AnA, Fingelkurts AlA (2005) Mapping of the brain operational architectonics. Chapter 2. In: Chen FJ (ed) Focus on brain mapping research.
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are being conducted to determine precisely the correlation between conscious and unconscious task deliberation in the realm of the global workspace.
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Fingelkurts AnA Fingelkurts AlA (2001). "Operational architectonics of the human brain biopotential field: towards solving the mind~brain problem".
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By identifying these correlations and the individual neurons that contribute to predictable EEG oscillations, scientists can determine which
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scale. As a result, mean frequencies in oscillatory bands cannot link together according to linearity of their mean frequencies. Instead,
475:) on the coordinated dynamical system has developed in the last five years as the number of TBI cases has risen from war-related injuries.
89:
It has been suggested that one integral facet of brain dynamics underlying conscious thought is the brain's ability to convert seemingly
1383:
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describing how people diffuse personal responsibility in emergency situations depending on the number of other individuals present.
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DeHaene, S.; L. Naccache (2001). "Toward a cognitive neuroscience of consciousness: basic evidence and a workspace framework".
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Jirsa, V.K.; A. Fuchs; J.A.S. Kelso (November 1998). "Connecting
Cortical and behavioral dynamics: bimanual coordination".
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Fingelkurts AnA Fingelkurts AnA (2006). "Timing in cognition and EEG brain dynamics: discreteness versus continuity".
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Fingelkurts AnA Fingelkurts AlA (2004). "Making complexity simpler: multivariability and metastability in the Brain".
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Fingelkurts, A.; A. Fingelkurts (2004). "Making complexity simpler: Multivariability and metastability in the brain".
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by describing mathematical formulae and paradigms governing the coupling of environmental stimuli to their effectors.
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17:
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The traditional EEG is still useful to investigate coordination between different parts of the brain. 40 Hz
511:
Proceedings of the Third IEEE International
Workshop on Cellular Neural Networks and their Applications (CNNA-94)
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Fuchs, A.; V.K. Jirsa (2000). "The HKB model revisited: How varying the degree of symmetry controls dynamics".
509:
Thiran, P; M Hasler (1994-12-18). "Information processing using stable and unstable oscillations: A tutorial".
366:. In this model, metastable interactions in the thalamocortical region cause a process of selectionism via
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One theory used to integrate the dynamic core with conscious thought involves a developing concept known as
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A popular experiment to demonstrate the global workspace hypothesis involves showing a subject a series of
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341:, which is a term to loosely describe the thalamocortical region believed to be the integration center of
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Kelso, J.A. Scott; et al. (1988). "Dynamic pattern generation in behavioral and neural systems".
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in his wake. To help prove the circuit-like amplification theory, research has shown that inducing
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regime can be found in many biological systems – for instance, in the output of a heartbeat in an
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313:) behavior. Further analysis of Phi may reveal the social and interpersonal implications of
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In EEG oscillations of neural networks, neighboring waveform frequencies are correlated on a
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1002:"Disentangling the dynamic core: a research program for a neurodynamics at the large-scale"
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would allow; and that their recognizable patterns are sometimes interrupted by periods of
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116:. This phase synchronization forms the basis of metastable behavior in neural networks.
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normally outputs a dynamical oscillatory waveform, but also has the ability to output a
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has been fueled by advancements in the methods by which computers model brain activity.
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waveform—but serves a unique purpose for phase synchrony in neuronal networks. At the
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Transition of parallel movement of index fingers to antiparallel, symmetric movement.
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contribute to what can be thought of as conscious response to environmental stimuli.
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131:. This regime describes an environment in which a noisy signal (also known as
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Bressler SL, Kelso JA (2001). "Cortical coordination dynamics and cognition".
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Center for
Complex Systems and Brain Sciences - Florida Atlantic University
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787:"Metastability, criticality and phase transitions in brain and its models"
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An interest in the effect of a traumatic or semi-traumatic brain injury (
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in long-distance connections corrupts performance in integrative models.
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Coordination
Dynamics. In Encyclopedia of Complexity and Systems Science
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History of coordination dynamics and the Haken-Kelso-Bunz (HKB) model
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in a constant state of transition between unstable and stable phase
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Seth, A.; B. Baars (2005). "Neural
Darwinism and consciousness".
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in a cooperative and coordinated manner, providing the basis for
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880:"The phi complex as a neuromarker of human social coordination"
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leads to the possibility that these different signals serve a
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described as a series of in-phase and out-of-phase movements.
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are linked according to their ability to couple with adjacent
52:
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ability to integrate several functional parts and to produce
429:. In almost every experiment, conscious input in word and
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A new theory called the phi complex has been developed by
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or chaotic signals into predictable oscillatory patterns.
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Neural
Darwinism: The Theory of Neuronal Group Selection
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nature as a function of a coordinated dynamical system.
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shows that neuronal groups existing in the cortex and
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A second theory of metastability involves a so-called
280:. Additionally, MEG has a response time of about one
445:
701:
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The operational architectonics theory of brain–mind
288:Social coordination dynamics and the phi complex
398:blocks of ice with his skis, initiates a giant
172:Oscillatory activity and coordination dynamics
147:) is inversely proportional to its frequency.
40:metastability and the underlying framework of
8:
785:Werner, A. G.; V.K. Jirsa (September 2007).
221:Evolution of cognitive coordination dynamics
563:. U.S.: Oxford University Press. pp.
1349:"The Human Brain and Behavior Laboratory"
1287:http://www.bm-science.com/team/chapt3.pdf
1017:
905:
895:
658:Collier, T.; Charles Taylor (July 2004).
135:) has been induced, where the amount of
501:
1043:"An update on global workspace theory"
878:Tognoli, E; et al. (March 2007).
660:"Self-organization in sensor networks"
664:J. Parallel and Distributed Computing
590:International Journal of Neuroscience
385:or coordination in the dynamic core.
7:
979:. New York, New York: Basic Books.
139:the signal outputs over a certain
119:Metastable behavior occurs at the
85:Frequency domains of metastability
14:
1047:Science and Consciousness Review
814:10.1016/j.biosystems.2006.12.001
1041:Baars, Bernard (October 2003).
1019:10.4067/s0716-97602003000100006
631:A.S. Kelso, James (July 2014).
1:
1283:Nova Science Publishers, Inc.
1135:10.1016/s1364-6613(00)01564-3
1080:10.1016/S0010-0277(00)00123-2
720:10.1016/S0167-9457(00)00025-7
1252:"Operational Architectonics"
943:10.1016/j.concog.2004.08.008
411:backward-masked visual words
269:large-scale electrode arrays
931:Consciousness and Cognition
637:. Springer. pp. 1–17.
451:The future of metastability
389:Global workspace hypothesis
306:Florida Atlantic University
304:and fellow researchers at
1400:
1384:Computational neuroscience
849:10.1162/089976698300016954
676:10.1016/j.jpdc.2003.12.004
215:self-organized criticality
18:computational neuroscience
1319:10.1007/s10339-006-0035-0
1222:10.1080/00207450490450046
1000:Le Van Quyen, M. (2003).
602:10.1080/00207450490450046
257:, reflecting the brain's
167:Theories of metastability
555:Buzsáki, György (2006).
519:10.1109/cnna.1994.381695
419:"ylteiuq speels god eht"
415:"the dog sleeps quietly"
1187:10.1023/A:1014427822738
897:10.1073/pnas.0611453104
755:10.1126/science.3281253
708:Human Movement Science
468:, and schizophrenia.
201:
145:power spectral density
323:alpha and omega-males
315:degenerative diseases
199:
559:Rhythms of the Brain
513:. pp. 127–136.
490:Electroencephalogram
485:Cognitive psychology
379:stochastic processes
278:holistic brain model
806:2007BiSys..90..496W
747:1988Sci...239.1513S
741:(4847): 1513–1520.
462:Parkinson's disease
458:Alzheimer's disease
427:prefrontal cortices
247:synaptic conduction
30:neural oscillations
1292:2007-09-27 at the
1285:, pp 59–98. URL =
837:Neural Computation
202:
42:nonlinear dynamics
1109:J. A. Scott Kelso
986:978-0-19-286089-7
890:(19): 8190–8195.
644:978-3-642-27737-5
574:978-0-19-530106-9
528:978-0-7803-2070-3
302:J. A. Scott Kelso
191:self-organization
106:phase transitions
98:logarithmic scale
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383:neural outgrowth
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66:neuronal network
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294:social cues
282:millisecond
240:synergistic
58:frequencies
1363:2007-11-26
1268:2007-12-02
1052:2007-11-26
794:Biosystems
689:2007-11-26
496:References
259:metastable
251:randomness
242:function.
231:gamma wave
133:pink noise
127:1/f regime
36:activity.
1068:Cognition
1006:Biol. Res
400:avalanche
236:amplitude
141:bandwidth
34:conscious
1378:Category
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1290:Archived
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479:See also
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368:re-entry
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317:such as
60:. Each
48:Overview
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614:S2CID
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533:S2CID
143:(its
137:power
91:noisy
64:in a
1323:PMID
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981:ISBN
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