“Papers of School of Cognitive Sciences”
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401. H. Khosravi and H. Soltanian-Zadeh,
Multi-surface quasi-isometric flattening of the cortex,
in: ISBI_2014, , 2014, pp. [abstract]
402. S. Movahedi, N. Mohajeri and H. Soltanian-Zadeh,
Connectivity Abnormalities in Autism Spectrum Disorder Patients: A Resting State fMRI Study,
in: The 22nd Iranian Conference on Electrical Engineering (ICEE 2014), May 20-22, 2014, Shahid Beheshti University, , 2014, pp. [abstract]
403. P. Zamani and H. Soltanian-Zadeh,
Compressive Sensing Cardiac Cine MRI Using Invertible Non-Linear Transform,
in: The 22nd Iranian Conference on Electrical Engineering (ICEE 2014), May 20-22, 2014, Shahid Beheshti University, , 2014, pp. [abstract]
404. A. Khadem and G.A. Hossein-Zadeh,
Quantification of the effects of volume conduction on the EEG/MEG connectivity estimates: an index of sensitivity to brain interactions,
Physiological Measurement 35(2014), 2149-2164 [abstract]
405. A. Khadem and G.A. Hossein-Zadeh,
Estimation of direct nonlinear effective connectivity using information theory and multilayer perceptron,
Journal of Neuroscience Methods 229(2014), 53-67 [abstract]
406. M. Latifi-Navid, K. Elisevich and H. Soltanian-Zadeh,
Algorithmic Analysis of Clinical and Neuropsychological Data in Localization-Related Epilepsy,
IJCMAM 4(2014), 33-58 [abstract]
407. N. Emadi, R. Rajimehr and H. Esteky,
High baseline activity in inferior temporal cortex improves neural and behavioral discriminability during visual categorization,
Frontiers in Systems Neuroscience 8(2014), 1-13 [abstract]
408. M. Behroozi and M.R. Daliri,
Predicting brain states associated with object categories from fMRI data,
Journal of Integrative Neuroscience 13(2014), 1-23 [abstract]
409. M. Ghodrati, K. Rajaei and R. Ebrahimpour,
The importance of visual features in generic vs. specialized object recognition: a computational study,
Frontiers in Computational Neuroscience (2014), 1-15 [abstract]
410. M. Ghodrati, A. Farzmahdi, K. Rajaei, R. Ebrahimpour and M. Khaligh-Razavi,
Feedforward object-vision models only tolerate small image variations compared to human,
Frontiers in Computational Neuroscience 8(2014), 1-17 [abstract]
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