Metastability, criticality and phase transitions in brain and its models

Werner, Gerhard

doi:10.1016/j.biosystems.2006.12.001

Cited by 145 publications

(134 citation statements)

References 122 publications

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“…[15] along with the assumption that the nonergodic nature of intermittency is a manifestation of criticality. This is the conviction that criticality, with its long-range correlation [18], may be the key condition for the origin of the intelligent behavior of cooperative systems, from a flock of birds [19,20] to the human brain [21,22]. Thus, the linear response of a cooperative system at criticality, discussed in this paper, affords at the same time an interesting contribution to understanding how these complex systems may respond to external stimuli.…”

Section: Introductionmentioning

confidence: 82%

Linear response at criticality

Bologna

Grigolini

2012

Phys. Rev. E

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We study a set of cooperatively interacting units at criticality, and we prove with analytical and numerical arguments that they generate the same renewal non-Poisson intermittency as that produced by blinking quantum dots, thereby giving a stronger support to the results of earlier investigation. By analyzing how this out-ofequilibrium system responds to harmonic perturbations, we find that the response can be described only using a new form of linear response theory that accounts for aging and the nonergodic behavior of the underlying process. We connect the undamped response of the system at criticality to the decaying response predicted by the recently established nonergodic fluctuation-dissipation theorem for dichotomous processes using information about the second moment of the fluctuations. We demonstrate that over a wide range of perturbation frequencies the response of the cooperative system is greatest when at criticality.

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Section: Introductionmentioning

confidence: 82%

Linear response at criticality

Bologna

Grigolini

2012

Phys. Rev. E

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“…Such time-varying functional properties readily lend themselves to a description by computational models from theoretical physics, for instance self-organized criticality and related frameworks. In these models, dissipative systems (like the brain) generate intrinsic fluctuations that transpire into complex and unpredictable behavior and thus account for unexplained yet functionally relevant variance in neural and behavioral recordings (43).…”

Section: Discussionmentioning

confidence: 99%

Spontaneous local variations in ongoing neural activity bias perceptual decisions

Hesselmann

Kell

Eger

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

312

254

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Neural variability in responding to identical repeated stimuli has been related to trial-by-trial fluctuations in ongoing activity, yet the neural and perceptual consequences of these fluctuations remain poorly understood. Using functional neuroimaging, we recorded brain activity in subjects who reported perceptual decisions on an ambiguous figure, Rubin's vase-faces picture, which was briefly presented at variable intervals of >20 s. Prestimulus activity in the fusiform face area, a cortical region preferentially responding to faces, was higher when subjects subsequently perceived faces instead of the vase. This finding suggests that endogenous variations in prestimulus neuronal activity biased subsequent perceptual inference. Furnishing evidence that evoked sensory responses, we then went on to show that the pre-and poststimulus activity interact in a nonlinear way and the ensuing perceptual decisions depend upon the prestimulus context in which they occur.fusiform face area ͉ ongoing activity ͉ BOLD fMRI ͉ prestimulus activity ͉ visual perception S ince the earliest neurophysiological recordings, two issues have puzzled brain researchers: why do, trial by trial, cortical responses to identical stimuli vary so much (1), and what is the functional significance of spontaneous neural activity, i.e., activity that cannot be accounted for by the experimental manipulation and that is thus usually discarded as unexplained variance? The two issues have in part been tied together by relating response variability to fluctuations in ongoing prestimulus activity (2, 3). In anesthetized animals, an excellent prediction of the actual response evoked in each trial was achieved by assuming a fixed stimulus-driven or task-related response from averaging and adding, trial by trial, this response to baseline activity (2). Evidence for the perceptual relevance of ongoing neural activity comes from monkey electrophysiology (4) and more recently human imaging studies (5) where periliminal stimuli were perceived when prestimulus activity was at higher levels, thus resembling effects from experimentally instructed allocation of attention. This all-or-none effect of ongoing activity on detection of periliminal stimuli could correspond to the well known behavioral effect of arousal and alertness on perceptual thresholds (6). Here, we address whether ongoing activity only impacts whether something can be perceived or whether it also influences what is perceived. We therefore investigated whether spontaneously occurring cortical activity variations in the seconds before input processing bias subsequent perceptual decisions on a suprathreshold but ambiguous visual input. We then addressed whether observed responses were simply the sum of evoked responses plus the baseline activity or whether baseline activity affected the evoked response, which would indicate an interaction between baseline activity and evoked components).Using functional magnetic resonance imaging (fMRI), we pursued this issue by asking subjects what they perceived each ...

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“…Behavioral correlates of transitions between metastable cortical states have been identified (Bressler & Kelso, 2001;Bressler, 2002;Kelso, 1995;Kelso & Engstrom, 2006). A comprehensive overview of stability, metastability, and transitions in brain activity is given in Le Van Quyen, Boucher et al (2001) and Werner (2007). Chaotic itinerancy (Tsuda, 2001) is a mathematical theory that describes the trajectory of a dynamical system, which intermittently visits ''attractor ruins'' as it traverses across the landscape.…”

Section: Introductionmentioning

confidence: 99%

The KIV model of intentional dynamics and decision making

Kozma

Freeman

2009

Neural Networks

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a b s t r a c tHuman cognition performs granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of fuzzy concepts and behaviors. These knowledge granules are stored and consequently accessed during action selection and decisions. A dynamical approach is presented here to interpret experimental findings using K (Katchalsky) models. In the K model, meaningful knowledge is repetitiously created and processed in the form of sequences of oscillatory patterns of neural activity distributed across space and time. These patterns are not rigid but flexible and intermittent; soon after they arise through phase transitions, they dissipate. Computational implementations demonstrate the operation of the model based on the principles of intentional brain dynamics.

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Metastability, criticality and phase transitions in brain and its models

Cited by 145 publications

References 122 publications

Linear response at criticality

Linear response at criticality

Spontaneous local variations in ongoing neural activity bias perceptual decisions

The KIV model of intentional dynamics and decision making

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