@article{Gravier201649,
title = {Neural network modelling of the influence of channelopathies on reflex visual attention},
author = {A Gravier and C Quek and W Duch and A Wahab and J Gravier-Rymaszewska},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955207541&doi=10.1007%2fs11571-015-9365-x&partnerID=40&md5=52f56b25f1d05a2d8eb0249e67e49f45},
doi = {10.1007/s11571-015-9365-x},
issn = {18714080},
year = {2016},
date = {2016-01-01},
journal = {Cognitive Neurodynamics},
volume = {10},
number = {1},
pages = {49-72},
publisher = {Springer Netherlands},
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. © 2015, Springer Science+Business Media Dordrecht.},
note = {cited By 8},
keywords = {Article, Artificial Neural Network, Attention, Autism, Calcium Channelopathy, Cell Structure, Cognition, Connectome, Electric Activity, Learning, Mathematical Analysis, Mathematical Model, Nerve Cell, Simulation, Visual Reflex},
pubstate = {published},
tppubtype = {article}
}

@article{Bhat2014851,
title = {Automated diagnosis of autism: In search of a mathematical marker},
author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f},
doi = {10.1515/revneuro-2014-0036},
issn = {03341763},
year = {2014},
date = {2014-01-01},
journal = {Reviews in the Neurosciences},
volume = {25},
number = {6},
pages = {851-861},
publisher = {Walter de Gruyter GmbH},
abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.},
note = {cited By 34},
keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis},
pubstate = {published},
tppubtype = {article}
}