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“Papers of School of Cognitive Sciences”

 

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  1. V. Malekian, A. Moghaddam, A. Akhavan and GH. Hossein-Zadeh,
Efficient de-noising of high-resolution fMRI using local and sub-band information,
Journal of Neuroscience Methods 331(2020), 1-10  [abstract]

   2. V. Malekian, Sh. Shalikar, F. Rastegar and A. Nasiraei Moghaddam,
SSFP-fMRI: Radial reading helps to Improve Temporal SNR,
in: Proc. Intl Soc Mag Reson Med. Conf. (Toronto, CA), 2019, , 2019, pp.   [abstract]

   3. V. Malekian and A. Moghaddam,
Motor Mapping for Pre-surgical Planning Using Seed-based Resting-State fMRI Approach,
in: 36th Proc. ESMRMb (Rotterdam, NL). Vol. 36. 2019, 36, 2019, pp.   [abstract]

   4. M. Hasantash, R. Lafer-Sousa, A. Afraz and B. Conway,
Paradoxical impact of memory on color appearance of faces,
Nature Communications 10(2019), 1-10  [abstract]

   5. A. Daneshi, H. Azarnoush, F. Towhidkhah, A. Gohari and A. Ghazizadeh,
Drift-diffusion explains response variability and capacity for tracking objects,
Scientific Reports 9(2019), 1-15  [abstract]

   6. V. Kozyrev, M. Daliri, P. Schwedhelm and S. Treue,
Strategic deployment of feature-based attentional gain in primate visual cortex,
Plos Biology (2019), 1-18  [abstract]

   7. M. Khamechian, V. Kozyrev, S. Treue, M. Esghaei and M. Daliri,
Routing information flow by separate neural synchrony frequencies allows for "functionally labeled lines" in higher primate cortex,
PNAS 116(2019), 12506-12515  [abstract]

   8. K. Rajaei, Y. Mohsenzadeh, R. Ebrahimpour and S. Khaligh-Razavi,
Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion,
PLOS Computational Biology  (to appear) [abstract]

   9. S. Vafaei Shooshtari, J. Esmaily Sadrabadi, Z. Azizi and Ebrahimpour. Reza,
Confidence Representation of Perceptual Decision by EEG and Eye Data in a Random Dot Motion Task,
Neuroscience 406(2019), 510-527  [abstract]

   10. S. Masoudnia, O. Mersa, B. Araabi, A. Vahabie, M. Sadeghi and M. Ahmadabadi,
Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs,
Expert Systems with Applications (2019),   [abstract]

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