ArticleCognitive neurodynamics2016
Neural network modelling of the influence of channelopathies on reflex visual attention.
Article in Cognitive neurodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level.Cognitive neurodynamics · 2019Article
- EEG classification of driver mental states by deep learning.Cognitive neurodynamics · 2018Article
- Simulation of retinal ganglion cell response using fast independent component analysis.Cognitive neurodynamics · 2018Article
- Vibrational resonance in a randomly connected neural network.Cognitive neurodynamics · 2018Article
- The informational entropy endowed in cortical oscillations.Cognitive neurodynamics · 2018Article
- Estimation of effective connectivity using multi-layer perceptron artificial neural network.Cognitive neurodynamics · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network's rate of failure to shift attention is lower than the control network's, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.