scholarly journals Random recurrent networks near criticality capture the broadband power distribution of human ECoG dynamics

2016 ◽  
Author(s):  
Rishidev Chaudhuri ◽  
Biyu He ◽  
Xiao-Jing Wang
Keyword(s):  
Power Spectrum ◽  
Power Distribution ◽  
Random Network ◽  
Field Potential ◽  
Power Spectra ◽  
Low Frequency ◽  
Recurrent Network ◽  
Network Activity ◽  
Brain Diseases ◽  
Low Frequency Power

The power spectrum of brain electric field potential recordings is dominated by an arrhythmic broadband signal but a mechanistic account of its underlying neural network dynamics is lacking. Here we show how the broadband power spectrum of field potential recordings can be explained by a simple random network of nodes near criticality. Such a recurrent network produces activity with a combination of a fast and a slow autocorrelation time constant, with the fast mode corresponding to local dynamics and the slow mode resulting from recurrent excitatory connections across the network. These modes are combined to produce a power spectrum similar to that observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such a network naturally converts input correlations across nodes into temporal autocorrelation of the network activity. Consequently, increased independence between nodes results in a reduction in low-frequency power, which offers a possible explanation for observed changes in ECoG power spectra during task performance. Lastly, changes in network coupling produce changes in network activity power spectra reminiscent of those seen in human ECoG recordings across different arousal states. This model thus links macroscopic features of the empirical ECoG power spectrum to a parsimonious underlying network structure and proposes potential mechanisms for changes in ECoG power spectra observed across behavioral and arousal states. This provides a computational framework within which to generate and test hypotheses about the cellular and network mechanisms underlying whole brain electrical dynamics, their variations across behavioral states as well as abnormalities associated with brain diseases.

Modulation of Neurocardiac Function by Oesophageal Stimulation in Humans

1997 ◽  
Vol 92 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Gervais Tougas ◽  
Markad Kamath ◽  
Geena Watteel ◽  
Debbie Fitzpatrick ◽  
Ernest L. Fallen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Power Spectrum ◽  
High Frequency ◽  
Low Frequency ◽  
Sensory Stimulation ◽  
Sensory Stimuli ◽  
Autonomic Reflexes ◽  
Low Frequency Power ◽  
Beat Heart

1. The heart and the oesophagus have similar sensory pathways, and sensations originating from the oesophagus are often difficult to differentiate from those of cardiac origin. We hypothesized that oesophageal sensory stimuli could alter neurocardiac function through autonomic reflexes elicited by these oesophageal stimuli. In the present study, we examined the neurocardiac response to oesophageal stimulation and the effects of electrical and mechanical oesophageal stimulation on the power spectrum of beat-to-beat heart rate variability in male volunteers. 2. In 14 healthy volunteers, beat-to-beat heart rate variability was compared at rest and during oesophageal stimulation, using either electrical (200 μs, 16 mA, 0.2 Hz) or mechanical (0.5 s, 14 ml, 0.2 Hz) stimuli. The power spectrum of beat-to-beat heart rate variability was obtained and its low- and high-frequency components were determined. 3. Distal oesophageal stimulation decreased heart rate slightly (both electrical and mechanical) (P < 0.005), and markedly altered heart rate variability (P < 0.001). Both electrical and mechanical oesophageal stimulation increased the absolute and normalized area of the high-frequency band within the power spectrum (P < 0.001), while simultaneously decreasing the low-frequency power (P < 0.005). 4. In humans, oesophageal stimulation, whether electrical or mechanical, appears to amplify respiratory-driven cardiac vagoafferent modulation while decreasing sympathetic modulation. The technique provides access to vagoafferent fibres and thus may yield useful information on the autonomic effects of visceral or oesophageal sensory stimulation.

scholarly journals Fundamental uncertainty levels of 21 cm power spectra from a delay analysis

2019 ◽  
Vol 487 (4) ◽  
pp. 5840-5853 ◽  
Author(s):  
Adam E Lanman ◽  
Jonathan C Pober
Keyword(s):  
Power Spectrum ◽  
Power Spectra ◽  
Low Frequency ◽  
Sample Variance ◽  
Model Parameters ◽  
Delay Analysis ◽  
Analysis Strategies ◽  
Murchison Widefield Array ◽  
Fundamental Uncertainty ◽  
Combining Estimates

Abstract Several experimental efforts are underway to measure the power spectrum of 21 cm fluctuations from the epoch of reionization (EoR) using low-frequency radio interferometers. Experiments like the Hydrogen Epoch of Reionization Array (HERA) and Murchison Widefield Array Phase II (MWA) feature highly redundant antenna layouts, building sensitivity through redundant measurements of the same angular Fourier modes, at the expense of diminished UV coverage. This strategy limits the numbers of independent samples of each power spectrum mode, thereby increasing the effect of sample variance on the final power spectrum uncertainty. To better quantify this effect, we measure the sample variance of a delay-transform based power spectrum estimator, using both analytic calculations and simulations of flat-spectrum EoR-like signals. We find that for the shortest baselines in HERA, the sample variance can reach as high as 20 per cent, and up to 30 per cent for the wider fields of view of the MWA. Combining estimates from all the baselines in a HERA- or MWA-like 37 element redundant hexagonal array can lower the variance to 1−3 per cent for some Fourier modes. These results have important implications for observing and analysis strategies, and suggest that sample variance can be non-negligible when constraining EoR model parameters from upcoming 21 cm data.

Analysis of turbulence power spectra and velocity correlations in a pipeline with obstructions

2017 ◽  
Vol 28 (02) ◽  
pp. 1750019 ◽  
Author(s):  
A. T. da Cunha Lima ◽  
I. C. da Cunha Lima ◽  
M. P. de Almeida
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Cross Sections ◽  
Power Spectra ◽  
Low Frequency ◽  
Frequency Range ◽  
Frequency Space ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
Low Frequency Power

We calculate the power spectral density and velocity correlations for a turbulent flow of a fluid inside a duct. Turbulence is induced by obstructions placed near the entrance of the flow. The power spectral density is obtained for several points at cross-sections along the duct axis, and an analysis is made on the way the spectra changes according to the distance to the obstruction. We show that the differences on the power spectral density are important in the lower frequency range, while in the higher frequency range, the spectra are very similar to each other. Our results suggest the use of the changes on the low frequency power spectral density to identify the occurrence of obstructions in pipelines. Our results show some frequency regions where the power spectral density behaves according to the Kolmogorov hypothesis. At the same time, the calculation of the power spectral densities at increasing distances from the obstructions indicates an energy cascade where the spectra evolves in frequency space by spreading the frequency amplitude.

scholarly journals Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dongbao Jia ◽  
Cunhua Li ◽  
Qun Liu ◽  
Qin Yu ◽  
Xiangsheng Meng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Frequency Characteristics ◽  
Brain Diseases ◽  
Time Dimension ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Low Frequency Power

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.

Sign in / Sign up

Export Citation Format

Share Document