ArticleCognitive neurodynamics2019
A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Biofeedback Training after Successful Inverted Internal Limiting Membrane (ILM)-Flap Technique for High Myopic Macular Hole.Journal of clinical medicine · 2023Article
- Frontal-occipital phase synchronization predicts occipital alpha power in perceptual decision-making.Cognitive neurodynamics · 2023Article
- Efficacy of MP-3 microperimeter biofeedback fixation training for low vision rehabilitation in patients with maculopathy.BMC ophthalmology · 2022Article
- Biophysical mechanism of the interaction between default mode network and working memory network.Cognitive neurodynamics · 2021Article
- Setting the space for deliberation in decision-making.Cognitive neurodynamics · 2021Review
- Synaptic dendritic activity modulates the single synaptic event.Cognitive neurodynamics · 2021Article
- Neural mechanism of visual information degradation from retina to V1 area.Cognitive neurodynamics · 2021Article
- Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity.Cognitive neurodynamics · 2021Article
- Ionic channel blockage in stochastic Hodgkin-Huxley neuronal model driven by multiple oscillatory signals.Cognitive neurodynamics · 2020Article
- A spiking neural network model of spatial and visual mental imagery.Cognitive neurodynamics · 2020Article
- Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.Neural plasticity · 2020Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin-Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABA
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