Transfer entropy (TE) is a nonlinear metric employed recently in polysomnography (PSG) recordings to quantify the topological characteristics of the brain-heart physiological network. The present study applies the TE to evaluate its usefulness to identify quantitative differences in PSG registers of patients diagnosed with oclusive sleep apnea (OSA), before and after a continuous positive air pressure (CPAP) therapy. PSG recordings corresponding to 19 OSA patients were analysed under the rationale that the set of EEG subbands represents the sympathetic activity of the autonomic nervous system (ANS), and the high frequency component of the heart rate variability (HRV) represents the parasympathetic activity. The TE was computed based on a binning estimation and the results were analyzed via effect size calculation. The results showed that the sympathetic activity is increased in the presence of OSA, which is represented by the increased flow of information among brain subsystems and dropping to values close to zero during CPAP therapy. In contrast, the parasympathetic activity showed to be reduced in the presence of OSA and augmented during the CPAP therapy.
We propose a model for a walker moving on an asymmetric periodic ratchet potential. The walker has two 'feet' represented as two finite-size particles coupled nonlinearly through a double-well potential. In contrast to linear coupling, the bistable potential admits a richer dynamics where the ordering of the particles can alternate. The transitions between the two stable points on the bistable potential, correspond to a walking with alternating particles. In our model, each particle is acted upon by independent white noises, modeling thermal noise, and additionally we have an external time-dependent force that drives the system out of equilibrium, allowing directed transport. This force can be common colored noise, periodic deterministic driving or fluctuations on the bistable potential. In the equilibrium case, where only white noise is present, we perform a bifurcation analysis which reveals different walking patterns available for various parameter settings. Numerical simulations showed the existence of current reversals and significant changes in the effective diffusion constant and in the synchronization index. We obtained an optimal coherent transport, characterized by a maximum dimensionless ratio of the current and the effective diffusion (Peclet number), when the periodicity of the ratchet potential coincides with the equilibrium distance between the two particles.
KEYWORDS: Neurons, Statistical analysis, Numerical simulations, Data processing, Stochastic processes, Data modeling, Sensors, Systems modeling, Particles, Signal detection
The detection of external stimuli by peripheral sensory neurons and
the downstream processing by cortical neurons quite often involves
the operational mode of bursting activity, i.e., periods of rapid firing alternating with intervals of quiescence. In some systems,
e.g. the electrosense of the paddlefish (Polyodon spathula), a
transition from tonic firing to the bursting regime can be induced
by external noise. Under the hypothesis that information about the
external stimulus is encoded in the statistics of the interspike intervals (ISIs) we quantify the information content of these noisy
bursters and the sensitivity to weak stimuli. Our analysis is based
on analytic methods and numerical simulations of effective burster
models.
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