scholarly journals Dynamic properties of internal noise probed by modulating binocular rivalry

2018 ◽  
Author(s):  
Daniel H. Baker ◽  
Bruno Richard
Keyword(s):  
Binocular Rivalry ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Amplitude Spectrum ◽  
Internal Noise ◽  
Internal Variability ◽  
External Noise ◽  
Neural Systems ◽  
Random Fashion ◽  
Noise Sequence

AbstractNeural systems are inherently noisy, and this noise can affect our perception from moment to moment. This is particularly apparent in binocular rivalry, where our perception of competing stimuli shown to the left and right eyes alternates over time in a seemingly random fashion. We investigated internal noise using binocular rivalry by modulating rivalling stimuli using dynamic sequences of external noise of various rates and amplitudes. As well as measuring the effect on dominance durations, we repeated each external noise sequence twice, and assessed the consistency of percepts across repetitions. External noise modulations with standard deviations above 4% contrast increased consistency scores above baseline, and were most effective at 1/8Hz. A computational model of rivalry in which internal noise has a 1/f (pink) temporal amplitude spectrum, and a standard deviation of 16%, provided the best account of our data, and was able to correctly predict perception in additional conditions. Our novel technique provides detailed estimates of the dynamic properties of internal noise during binocular rivalry, and by extension the stochastic processes that drive our perception and other types of spontaneous brain activity.Significance statementAlthough our perception of the world appears constant, sensory representations are variable because of the ‘noisy’ nature of biological neurons. Here we used a binocular rivalry paradigm, in which conflicting images are shown to the two eyes, to probe the properties of this internal variability. Using a novel paradigm in which the contrasts of rivalling stimuli are modulated by two independent external noise streams, we infer the amplitude and character of this internal noise. The temporal amplitude spectrum of the noise has a 1/f spectrum, similar to that of natural visual input, and consistent with the idea that the visual system evolved to match its environment.

Acoustic Performance of Louvred Facades for Brisbane Domestic Airport: An Integrated Approach

2012 ◽  
Author(s):  
Frank Butera ◽  
Keith Hewett
Keyword(s):  
Natural Ventilation ◽  
Transmission Loss ◽  
Internal Noise ◽  
Building Design ◽  
Integrated Approach ◽  
External Noise ◽  
Climatic Conditions ◽  
Sustainable Building ◽  
Heating And Cooling ◽  
Airport Terminals

Maximising cross ventilation is a low energy method of naturally ventilating and providing heating and cooling to deep plan spaces. Significant reduction in the emission of greenhouse gases can be achieved through minimising the use of mechanical systems in regions with climatic conditions that support the use of natural ventilation. Arup has provided input into the design of a louvered facade for the control of external noise for Brisbane Domestic Airport. A full scale prototype facade was constructed and noise transmission loss measurements were undertaken. The results indicate that significant noise reduction can be achieved to enable compliance with the internal noise limits for airport terminals, whilst using natural ventilation. The findings from this research will directly benefit building designers and innovators in the pursuit of achieving sustainable building design.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings

2008 ◽  
pp. 84-92
Author(s):  
Javier Escudero ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Jesús Poza ◽  
Alberto Fernández
Keyword(s):  
Magnetic Fields ◽  
Quantum Interference ◽  
Brain Activity ◽  
External Noise ◽  
Low Noise ◽  
High Temporal Resolution ◽  
Artifact Detection ◽  
Mental Diseases ◽  
Electroencephalogram Eeg ◽  
The Brain

The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in contrast to EEG, MEG does not depend on any reference point. Moreover, the magnetic fields are less distorted than the electric ones by the skull and the scalp (Hämäläinen et al., 1993). Despite these advantages, the use of MEG data involves some problems. One of the most important difficulties is that MEG recordings may be severely contaminated by additive external noise due to the intrinsic weakness of the brain magnetic fields. Hence, MEG must be recorded in magnetically shielded rooms with low-noise SQUID (Superconducting QUantum Interference Devices) gradiometers (Hämäläinen et al., 1993).

WATERMARK DETECTION USING DYNAMIC STOCHASTIC RESONANCE

2013 ◽  
Vol 12 (03) ◽  
pp. 1350010 ◽  
Author(s):  
RAJIB KUMAR JHA ◽  
APOORV CHATURVEDI ◽  
RAJLAXMI CHOUHAN
Keyword(s):  
Stochastic Resonance ◽  
Spread Spectrum ◽  
Internal Noise ◽  
External Noise ◽  
Detection Technique ◽  
Discrete Wavelet ◽  
Detection Techniques ◽  
Dynamic Stochastic Resonance ◽  
Watermark Embedding ◽  
Dynamic Stochastic

In this paper, a dynamic stochastic resonance (DSR) based watermark detection technique in discrete wavelet transform (DWT) domain is presented. Pseudo random bit sequence having certain seed value is considered as a watermark. Watermark embedding is done by embedding random bits in spread-spectrum fashion to the significant DWT coefficients. Watermark detection is quantitatively characterized by the value of correlation. The performance of watermark detection is improved by DSR which is an iterative process that utilizes the internal noise present in the image or external noise which is added during attacks. Even under various noise attacks, geometrical distortions, image enhancement and compression attacks, the DSR-based random bits detection is observed to give noteworthy improvement over existing watermark detection techniques. DSR-based technique is also found to give better detection performance when compared with the suprathreshold stochastic resonance-based detection technique.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

scholarly journals High internal noise and poor external noise filtering characterize perception in autism spectrum disorder

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Woon Ju Park ◽  
Kimberly B. Schauder ◽  
Ruyuan Zhang ◽  
Loisa Bennetto ◽  
Duje Tadin
Keyword(s):  
Autism Spectrum Disorder ◽  
Internal Noise ◽  
External Noise ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Noise Filtering

scholarly journals Evolution of the Dynamic Changes in Functional Cerebral Oxidative Metabolism from Tissue Mitochondria to Blood Oxygen

2012 ◽  
Vol 32 (4) ◽  
pp. 745-758 ◽  
Author(s):  
Alberto L Vazquez ◽  
Mitsuhiro Fukuda ◽  
Seong-Gi Kim
Keyword(s):  
Metabolic Rate ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Oxygen Electrode ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygenation ◽  
Blood Oxygen ◽  
Autofluorescence Imaging ◽  
Response Condition ◽  
Intrinsic Signal

The dynamic properties of the cerebral metabolic rate of oxygen consumption (CMRO2) during changes in brain activity remain unclear. Therefore, the spatial and temporal evolution of functional increases in CMRO2 was investigated in the rat somato-sensory cortex during forelimb stimulation under a suppressed blood flow response condition. Temporally, stimulation elicited a fast increase in tissue mitochondria CMRO2 described by a time constant of ~ 1 second measured using flavoprotein autofluorescence imaging. CMRO2-driven changes in the tissue oxygen tension measured using an oxygen electrode and blood oxygenation measured using optical imaging of intrinsic signal followed; however, these changes were slow with time constants of ~ 5 and ~ 10 seconds, respectively. This slow change in CMRO2-driven blood oxygenation partly explains the commonly observed post-stimulus blood oxygen level-dependent (BOLD) undershoot. Spatially, the changes in mitochondria CMRO2 were similar to the changes in blood oxygenation. Finally, the increases in CMRO2 were well correlated with the evoked multi-unit spiking activity. These findings show that dynamic CMRO2 calculations made using only blood oxygenation data (e.g., BOLD functional magnetic resonance imaging (fMRI)) do not directly reflect the temporal changes in the tissue's mitochondria metabolic rate; however, the findings presented can bridge the gap between the changes in cellular oxidative rate and blood oxygenation.

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