Microstructure of cortical activity during sleep reflects respiratory events and state of daytime vigilance

Traxdorf, Maximilian; Krauß, Patrick; Schilling, Achim; Schulze, Holger; Tziridis, Konstantin

doi:10.1007/s11818-019-0201-0

Cited by 16 publications

(14 citation statements)

References 17 publications

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“…Indeed, we have confirmed this hypothesis in former work [8], where we applied a previously developed new method for analyzing and comparing spatiotemporal cortical activation patterns [9]. In this context, we have also analyzed the micro-structure of cortical activity during sleep and found that it reflects respiratory events and the state of daytime vigilance [10]. Moreover, we have developed a general method to quantify the separability of point clusters in high-dimensional state-spaces [11].…”

Section: Introductionsupporting

confidence: 72%

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

Metzner

Schilling

Traxdorf

et al. 2021

Preprint

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In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a superstatistical analysis by computing 'hyper-parameters', such as the standard deviation, kurtosis and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than subdivided process that controls human sleep.

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Section: Introductionsupporting

confidence: 72%

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

Metzner

Schilling

Traxdorf

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Indeed, we have confirmed this hypothesis in former work 8 , where we applied a previously developed method for analyzing and comparing spatiotemporal cortical activation patterns 9 . In this context, we have also analyzed the micro-structure of cortical activity during sleep and found that it reflects respiratory events and the state of daytime vigilance 10 . Moreover, we have developed a general method to quantify the separability of point clusters in high-dimensional state spaces 11 .…”

Section: Introductionmentioning

confidence: 99%

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

et al. 2021

Self Cite

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In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces.

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“…For instance, MDS has already been applied to visualize for instance word class distributions of different linguistic corpora 48 , hid-den layer representations (embeddings) of artificial neural networks 49 , 50 , structure and dynamics of recurrent neural networks 51 – 53 , or brain activity patterns assessed during e.g. pure tone or speech perception 48 , 54 , or even during sleep 55 , 56 . In all these cases the apparent compactness and mutual overlap of the point clusters permits a qualitative assessment of how well the different classes separate.…”

Section: Methodsmentioning

confidence: 99%

Neural network based successor representations to form cognitive maps of space and language

Paul

Schlieker

Schilling

et al. 2022

Sci Rep

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How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.

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Microstructure of cortical activity during sleep reflects respiratory events and state of daytime vigilance

Cited by 16 publications

References 17 publications

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

Neural network based successor representations to form cognitive maps of space and language

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