1,721,270 research outputs found
A neurodynamical model to simulate neural activities in visual attention experiments
No full textIn the present work we follow a computational neuroscience approach in order to study the role of attention in visual perception. According to the biased competition hypothesis, when multiple stimuli are present in the visual field, populations of neurons are activated that engage in competitive interactions. Experimental studies in humans using functional magnetic resonance imaging (fMRI) techniques confirm such hypothesis. Here, we present a model to simulate these experimental data within a biased competition neurodynamics. The model consists of several interconnected modules which can be related with the different areas of the dorsal and ventral paths of the visual cortex. © 2002 Elsevier Science B.V. All rights reserved
Large-scale neural model for visual attention: Integration of experimental single cell and fMRI data
No full textA computational neuroscience framework is proposed to better understand the role and the neuronal correlate of spatial attention modulation in visual perception. The model consists of several interconnected modules that can be related to the different areas of the dorsal and ventral paths of the visual cortex. Competitive neural interactions are implemented at both microscopic and interareal levels, according to the biased competition hypothesis. This hypothesis has been experimentally confirmed in studies in humans using functional magnetic resonance imaging (fMRI) techniques and also in single-cell recording studies in monkeys. Within this neurodynamical approach, numerical simulations are carried out that describe both the fMRI and the electrophysiological data. The proposed model draws together data of different spatial and temporal resolution, as are the above-mentioned imaging and single-cell results
Selective attention in visual search via synchronization of phase oscillators in a multimodular neural network
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Feature-based attention in human visual cortex: Simulation of fMRI data
No full textWe use a computational neuroscience approach to study the role of feature-based attention in visual perception. This model is used to numerically simulate a visual attention experiment. The neurodynamical system consists of many interconnected modules that can be related to the dorsal and ventral paths of the visual cortex. The biased competition hypothesis is taken into account within the model. From the experimental point of view, measurements exist, which confirm that feature-based attention influences visual cortical responses to stimuli outside the attended location. These measurements show that attention to a given stimulus attribute (in this case "color red") increases the response of cortical visual areas to a spatially distant, ignored stimulus that shares the same attribute. Our neurodynamical model is used to numerically compute the neural activity of area V4 corresponding to such ignored stimulus, giving a good description of the experimental data. © 2003 Elsevier Inc. All rights reserved
The Power of Synchronous Rhythms: Self-Similarity in Phase Dynamics, Neural Masses, and the Brain
Full text linkDaffertshofer, A. [Promotor]Deco, G. [Promotor
Systems-level neuronal modeling of visual attentional mechanisms
No full textWe review different functions involved in visual perception that have been integrated by a model based on the biased competition hypothesis. Attentional top-down bias guides the dynamics to concentrate at a given spatial location or on given features. The model integrates, in a unifying form, the explanation of several existing types of experimental data obtained at different levels of investigation. At the microscopic level, single cell recordings are simulated. At the mesoscopic level of cortical areas, results of functional magnetic resonance imaging (fMRI) studies are reproduced. Finally, at the macroscopic level, psychophysical experiments like visual search tasks are also described by the model
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