The multiple time scales of sleep dynamics as a challenge for modelling the sleeping brain

Olbrich, Eckehard; Claussen, Jens Christian; Achermann, Peter

doi:10.1098/rsta.2011.0082

Cited by 42 publications

(27 citation statements)

References 57 publications

(119 reference statements)

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“…Experimental evidence of chimeras has only recently been provided for optical [16], chemical [17], mechanical [18] and electronic [19] systems. These peculiar hybrid states may also account for the observation of partial synchrony in neural activity [20], like unihemispheric sleep, i.e., the ability of some birds or dolphins to sleep with one half of their brain while the other half remains aware [21,22]. Chimera states have been initially found for phase oscillators [2], and they typically occur in networks with nonlocal coupling.…”

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confidence: 99%

Chimera Death: Symmetry Breaking in Dynamical Networks

2014

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For a network of generic oscillators with nonlocal topology and symmetry-breaking coupling we establish novel partially coherent inhomogeneous spatial patterns, which combine the features of chimera states (coexisting incongruous coherent and incoherent domains) and oscillation death (oscillation suppression), which we call chimera death. We show that due to the interplay of nonlocality and breaking of rotational symmetry by the coupling two distinct scenarios from oscillatory behavior to a stationary state regime are possible: a transition from an amplitude chimera to chimera death via in-phase synchronized oscillations, and a direct abrupt transition for larger coupling strength. Spontaneous symmetry breaking in a complex dynamical system is a fundamental and universal phenomenon which occurs in diverse fields such as physics, chemistry, and biology [1]. It implies that processes occurring in nature favor a less symmetric configuration, although the underlying principles can be symmetric. This intriguing concept has recently gained renewed interest generated by the enormous burst of works on chimera states and on oscillation death, which have emerged independently. In this Letter we draw a relation between these two.Chimera states correspond to the situation when an ensemble of identical elements self-organizes into two coexisting and spatially separated domains with dramatically different behavior, i.e., spatially coherent and incoherent oscillations [2,3]. They have been the subject of intensive theoretical investigations, e.g. [4][5][6][7][8][9][10][11][12][13][14][15]. Experimental evidence of chimeras has only recently been provided for optical [16], chemical [17], mechanical [18] and electronic [19] systems. These peculiar hybrid states may also account for the observation of partial synchrony in neural activity [20], like unihemispheric sleep, i.e., the ability of some birds or dolphins to sleep with one half of their brain while the other half remains aware [21,22]. Chimera states have been initially found for phase oscillators [2], and they typically occur in networks with nonlocal coupling. Recently, for globally coupled oscillators it has been shown that such spatio-temporal patterns can be also connected to the amplitude dynamics (amplitude-mediated chimeras) [23,24]. However, the global coupling topology does not provide a clear notion of space, which is crucial for chimera states. A further open question is whether chimeras can be extended to more general symmetry breaking states.Another fascinating effect which requires the break-up of the system's symmetry as a crucial ingredient is oscillation death which refers to stable inhomogeneous steady states (IHSS) which are created through the coupling of self-sustained oscillators. This regime occurs when a ho- * corresponding author: schoell@physik.tu-berlin.de mogeneous steady state splits into at least two distinct branches -upper and lower -which represent a newly created IHSS [25][26][27][28]. For a network of coupled elements oscillation death ...

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confidence: 99%

Chimera Death: Symmetry Breaking in Dynamical Networks

2014

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“…The feed-forward and feed-back circuitry between the thalamus and the cortex has long since been known to play a key role in modulating brain rhythms associated with the various sleep stages as well as the sleep-wake transition (Steriade et al, 1993; Steriade, 2003, 2005; Crunelli et al, 2011). Computational models of the TCT brain circuit have therefore been the basis for studying neuronal mechanisms in sleep (Lumer et al, 1997a; Hill and Tononi, 2005; Traub et al, 2005; Bojak et al, 2011; Olbrich et al, 2011; Robinson et al, 2011) as well as in conditions where the EEG is qualitatively similar to certain sleep stages such as epilepsy (Breakspear et al, 2006) and under anaesthesia (Hutt and Longtin, 2010). While all such models refer to a similar holistic structure of the thalamocortical circuit, the models' internal structure, simulation platforms and parameterizations are significantly diverse.…”

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confidence: 99%

Engineering a thalamo-cortico-thalamic circuit on SpiNNaker: a preliminary study toward modeling sleep and wakefulness

Bhattacharya¹,

Patterson²,

Galluppi³

et al. 2014

Front. Neural Circuits

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We present a preliminary study of a thalamo-cortico-thalamic (TCT) implementation on SpiNNaker (Spiking Neural Network architecture), a brain inspired hardware platform designed to incorporate the inherent biological properties of parallelism, fault tolerance and energy efficiency. These attributes make SpiNNaker an ideal platform for simulating biologically plausible computational models. Our focus in this work is to design a TCT framework that can be simulated on SpiNNaker to mimic dynamical behavior similar to Electroencephalogram (EEG) time and power-spectra signatures in sleep-wake transition. The scale of the model is minimized for simplicity in this proof-of-concept study; thus the total number of spiking neurons is ≈1000 and represents a “mini-column” of the thalamocortical tissue. All data on model structure, synaptic layout and parameters is inspired from previous studies and abstracted at a level that is appropriate to the aims of the current study as well as computationally suitable for model simulation on a small 4-chip SpiNNaker system. The initial results from selective deletion of synaptic connectivity parameters in the model show similarity with EEG power spectra characteristics of sleep and wakefulness. These observations provide a positive perspective and a basis for future implementation of a very large scale biologically plausible model of thalamo-cortico-thalamic interactivity—the essential brain circuit that regulates the biological sleep-wake cycle and associated EEG rhythms.

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“…Oscillations of brain activity observed during sleep are complex and span multiple time and spatial scales, ranging from characteristic oscillations such as slow waves and sleep spindles, which span several seconds in duration, to transitions between the different sleep stages, which take places on a time scale of minutes, to the dynamical processes of sleep regulation, with characteristic time constants in the range of hours. The challenge of modelling the dynamics on these different time scales is discussed from a time-series analysis point of view in the contribution of Olbrich et al [36].…”

Section: The Complex Dynamics Of the Sleeping Brainmentioning

confidence: 99%

“…On the other hand, nonlinear measures-in particular, synchronization and interdependence measures-are applied without having a full model of the dynamics of the underlying system. In this issue, Olbrich et al [36] use linear time-series models to describe short segments of the sleep EEG, Seth et al [34] employ the Granger causality to define causal density as a quantitative measure that is sensitive to levels of consciousness, and Pascual-Marqui et al [35] propose the combination of source localization with coherence-based measures for the analysis of functional connectivity.…”

Section: The Complex Dynamics Of the Sleeping Brainmentioning

confidence: 99%

The sleeping brain as a complex system

Olbrich

Achermann

Wennekers

2011

Phil. Trans. R. Soc. A.

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'Complexity science' is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue 'The complexity of sleep' aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.

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The multiple time scales of sleep dynamics as a challenge for modelling the sleeping brain

Cited by 42 publications

References 57 publications

Chimera Death: Symmetry Breaking in Dynamical Networks

Chimera Death: Symmetry Breaking in Dynamical Networks

Engineering a thalamo-cortico-thalamic circuit on SpiNNaker: a preliminary study toward modeling sleep and wakefulness

The sleeping brain as a complex system

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