Suppression of synchronization is a central issue of the Internet congestion control. Current desynchronization scheme suffers from the breakdown of fairness. We propose a desynchronization scheme by introducing state...
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Suppression of synchronization is a central issue of the Internet congestion control. Current desynchronization scheme suffers from the breakdown of fairness. We propose a desynchronization scheme by introducing state feedback controllers in such a way that the sum of all components of the eigenvectors of the linearized system around the equilibrium is zero. With the aid of the method of multiple scales, the conditions on the values of controller parameters such that the desynchronous oscillation arises are obtained and two different patterns of desynchronization, i.e., discrete wave and anti-synchronization are observed, depending on the parity of number of sources. Numerical simulations are carried out to validate the theoretical analysis and the effectiveness of the proposed scheme is confirmed. (C) 2017 Elsevier B.V. All rights reserved.
The question of how perception arises from neuronal activity in the visual cortex is of fundamental importance in cognitive neuroscience. To address this question, we adopt a unique experimental paradigm in which bist...
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The question of how perception arises from neuronal activity in the visual cortex is of fundamental importance in cognitive neuroscience. To address this question, we adopt a unique experimental paradigm in which bistable structure-from-motion (SFM) stimuli are employed to dissociate the visual input from perception while monitoring the cortical neural activity called local field potential (LFP). Consequently, the stimulus-evoked activity of UP is not related to perception but the oscillatory induced activity of LFP may be percept-related. In this paper we focus on extracting the percept-related features of the induced activity from UP in a monkey's Visual cortex for decoding its bistable structure-from-motion perception. We first estimate the stimulus-evoked activity via a wavelet-based method and remove it from the single-trial LFP. We then use the common spatial patterns (CSP) approach to design spatial filters to extract the percept-related features from the remaining induced activity. We exploit the linear discriminant analysis (LDA) classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that our approach has excellent performance in estimating the stimulus-evoked activity, outperforming the Wiener filter, least mean square (LMS), and a local regression method called the locally weighted scatterplot smoothing (LOWESS), and that our approach is effective in extracting the discriminative features of the percept-related induced activity from LFP, which leads to excellent decoding performance. We also discover that the enhanced gamma band synchronization and reduced alpha band desynchronization may be the underpinnings of the induced activity. (C) 2009 Elsevier Ltd. All rights reserved.
synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan o...
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synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.
Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and i...
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Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models. We propose an alternative model that reproduces experimental findings of synchronized and desynchronized fast oscillations more closely. This model is based on technical considerations concerning improved pattern separation in associative memories on the one hand, and on known properties of the visual cortex on the other. It consists of two reciprocally connected areas, one corresponding to a peripheral visual area (P), the other a central association area (C). P implements the orientation-selective subsystem of the primary visual cortex, while C was modeled as an associative memory with connections formed by Hebbian learning of all assemblies corresponding to stimulus objects. Spiking neurons including habituation and correlated noise were incorporated as well as realistic synaptic delays. Three learned stimuli were presented simultaneously and correlation analysis was performed on spike recordings. Generally, we found two states of activity: (i) relatively slow and unordered oscillations at about 20-25 Hz, synchronized only within small regions;and (ii) faster and more precise oscillations around 50-60 Hz, synchronized over the whole simulated area. The neuron groups representing one stimulus tended to be simultaneously in either the slow or the fast state. At each particular time, only one assembly was found to be in the fast state. Activation of the three assemblies switched on a time scale of 100 ms. This can be interpreted as self-generated attention switching. On the time scale corresponding to gamma oscillations, cross correlations between local neuron groups were either modulated or flat. Modulated correlograms resulted if the groups coded features corresponding to a common object. Otherwise, the correlograms
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