How Precise is Neuronal Synchronization?

1995 ◽  
Vol 7 (3) ◽  
pp. 469-485 ◽  
Author(s):  
Peter König ◽  
Andreas K. Engel ◽  
Pieter R. Roelfsema ◽  
Wolf Singer
Keyword(s):  
Cortical Neurons ◽  
Visual Stimuli ◽  
Network Models ◽  
Movement Direction ◽  
Phase Lag ◽  
Graded Responses ◽  
Spatially Distributed ◽  
The Difference ◽  
Coarse Coding ◽  
Zero Phase

Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that features of a stimulus are represented by the graded responses of a population of optimally and suboptimally activated cells. To resolve this issue we investigated the temporal relationship between responses of optimally and suboptimally stimulated neurons in area 17 of cat visual cortex. We find that optimally and suboptimally activated cells can synchronize their responses with a precision of a few milliseconds. However, there are consistent and systematic deviations of the phase relations from zero phase lag. Systematic variation of the orientation of visual stimuli shows that optimally driven neurons tend to lead over suboptimally activated cells. The observed phase lag depends linearly on the stimulus orientation and is, in addition, proportional to the difference between the preferred orientations of the recorded cells. Similar effects occur when testing the influence of the movement direction and the spatial frequency of visual stimuli. These results suggest that binding by synchrony can be used to define assemblies of neurons representing a coarse-coded stimulus. Furthermore, they allow a quantitative test of neuronal network models designed to reproduce physiological results on stimulus-specific synchronization.

scholarly journals Functional Stratification of Cancer Drugs through Integrated Network Similarity

2022 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Seyma Unsal Beyge
Keyword(s):  
Cell Lines ◽  
Drug Targets ◽  
Hdac Inhibitors ◽  
Treatment Strategies ◽  
Network Models ◽  
Drug Combinations ◽  
Multiple Cancer ◽  
Integrated Network ◽  
Drug Molecules ◽  
The Difference

Abstract Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

scholarly journals Near-zero phase-lag hyperscanning in a novel wireless EEG system

2021 ◽  
Author(s):  
Chun-Hsiang Chuang ◽  
Shao-Wei Lu ◽  
Yiping Chao ◽  
Po-Hsun Peng ◽  
Hao-Che Hsu ◽  
...  
Keyword(s):  
Phase Lag ◽  
Wireless Eeg ◽  
Zero Phase

Three-phase lag model of thermo-elastic interaction in a 2D porous material due to pulse heat flux

2020 ◽  
Vol 30 (12) ◽  
pp. 5191-5207 ◽  
Author(s):  
Aatef Hobiny ◽  
Faris S. Alzahrani ◽  
Ibrahim Abbas
Keyword(s):  
Volume Fraction ◽  
Elastic Interaction ◽  
Phase Lag ◽  
Thermoelastic Wave ◽  
Content Type ◽  
Three Phase ◽  
Stress Components ◽  
Eigen Values ◽  
Lag Model ◽  
The Difference

Purpose The purposes of this study, a generalized model for thermoelastic wave under three-phase lag (TPL) model is used to compute the increment of temperature, the components of displacement, the changes in volume fraction field and the stress components in a two-dimension porous medium. Design/methodology/approach By using Laplace-Fourier transformations with the eigen values methodologies, the analytical solutions of all physical variables are obtained. Findings The derived methods are estimated with numerical outcomes which are applied to the porous media in simplified geometry. Originality/value Finally, the outcomes are represented graphically to display the difference among the models of the TPL and the Green and Naghdi (GNIII) with and without energy dissipations.

scholarly journals Real-Time Guitar Amplifier Emulation with Deep Learning

2020 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Alec Wright ◽  
Eero-Pekka Damskägg ◽  
Lauri Juvela ◽  
Vesa Välimäki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Reference Signal ◽  
Network Models ◽  
Neural Network Models ◽  
Desktop Computer ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
The Difference

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

A NEW METHODOLOGY FOR THE CONSTRUCTION OF OPTIMIZED RUNGE–KUTTA–NYSTRÖM METHODS

2011 ◽  
Vol 22 (06) ◽  
pp. 623-634 ◽  
Author(s):  
D. F. PAPADOPOULOS ◽  
T. E. SIMOS
Keyword(s):  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
First Integrals ◽  
New Method ◽  
Phase Lag ◽  
Nyström Methods ◽  
Algebraic Order ◽  
Runge Kutta ◽  
First Time ◽  
Zero Phase

In this paper, a new Runge–Kutta–Nyström method of fourth algebraic order is developed. The new method has zero phase-lag, zero amplification error and zero first integrals of the previous properties. Numerical results indicate that the new method is very efficient for solving numerically the Schrödinger equation. We note that for the first time in the literature we use the requirement of vanishing the first integrals of phase-lag and amplification error in the construction of efficient methods for the numerical solution of the Schrödinger equation.

Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency

1997 ◽  
Vol 9 (6) ◽  
pp. 1251-1264 ◽  
Author(s):  
Roger D. Traub ◽  
Miles A. Whittington ◽  
John G. R. Jefferys
Keyword(s):  
Pyramidal Cells ◽  
Sensory Stimulation ◽  
Gamma Oscillations ◽  
Feature Binding ◽  
Phase Lag ◽  
Cell Groups ◽  
Phase Lags ◽  
Zero Phase

Gamma-frequency electroencephalogram oscillations may be important for cognitive processes such as feature binding. Gamma oscillations occur in hippocampus in vivo during the theta state, following physiological sharp waves, and after seizures, and they can be evoked in vitro by tetanic stimulation. In neocortex, gamma oscillations occur under conditions of sensory stimulation as well as during sleep. After tetanic or sensory stimulation, oscillations in regions separated by several millimeters or more occur at the same frequency, but with phase lags ranging from less than 1 ms to 10 ms, depending on the conditions of stimulation. We have constructed a distributed network model of pyramidal cells and interneurons, based on a variety of experiments, that accounts for near-zero phase lag synchrony of oscillations over long distances (with axon conduction delays totaling 16 ms or more). Here we show that this same model can also account for fixed positive phase lags between nearby cell groups coexisting with near-zero phase lags between separated cell groups, a phenomenon known to occur in visual cortex. The model achieves this because interneurons fire spike doublets and triplets that have average zero phase difference throughout the network; this provides a temporal framework on which pyramidal cell phase lags can be superimposed, the lag depending on how strongly the pyramidal cells are excited.

Reversal of Asymmetry in Human Perceptual Performance as a Function of Labeling, Mode of Response, and Familiarity

1981 ◽  
Vol 52 (1) ◽  
pp. 183-193 ◽  
Author(s):  
Henry L. Dee ◽  
H. Julia Hannay
Keyword(s):  
Visual Field ◽  
Left Visual Field ◽  
Hemispheric Asymmetry ◽  
Visual Complexity ◽  
Visual Stimuli ◽  
High Complexity ◽  
Hemispheric Processing ◽  
Verbal Labels ◽  
Perceptual Performance ◽  
The Difference

It has previously been demonstrated that certain characteristics of the stimulus, specifically visual complexity and verbal association value, as well as mnemonic factors are important in producing the usually obtained asymmetry in human perceptual performance, and thus presumably, hemispheric asymmetry of function. The present research demonstrated that the usual superiority of the left visual field for high-complexity, low-association-value visual forms can be reversed by the acquisition and use of verbal labels for such stimuli but is only attenuated when the labels are not used to respond to the stimuli. Simple familiarity with the visual stimuli attenuated the difference between the fields, but here there was no reversal. Implications of these results for hemispheric processing are discussed.

Joint Combat System of Systems Modeling and Effectiveness Analysis Based on Complex Networks

2012 ◽  
Vol 591-593 ◽  
pp. 1589-1592 ◽  
Author(s):  
Yan Bo Qi ◽  
Liu Zhong ◽  
Le Zhang ◽  
Jiang Hu Xu
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
System Of Systems ◽  
Clustering Coefficient ◽  
Network Effect ◽  
Scale Free ◽  
Effectiveness Analysis ◽  
Joint Operations ◽  
The Difference ◽  
Basic Network

How to describe the difference among various combat networks and measure their effectiveness is an important problem in combat analysis. In this paper three basic network models are developed based on the theory of complex networks and a new method is put forward for measuring the network effect of combat SoS. Numerical comparisons of the three combat network models indicate that though integrated joint operations network has the highest networked effects in networked effectiveness and clustering coefficient, but when considering the Average Path Length index, it has the lowest effectiveness. The results also suggest that the degree distribution of integrated joint operations network is scale-free thus it has the highest survivability.

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