“Papers of School of Cognitive Sciences”
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61. S. Eskandari, A. Rezayof, S. Asghari and S. Hashemizadeh ,
Neurobiochemical characteristics of arginine-rich peptides explain their potential therapeutic efficacy in neurodegenerative diseases,
Neuropeptides 101(2023), [abstract]
62. M. Parsa, H. Yousefi Rad, H. Vaezi, G. Hossein-Zadeh, S. Setarehdan, R. Rostami , H. Rostami and A. Vahabie,
EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions,
Computer Methods and Programs in Biomedicine 240(2023), [abstract]
63. J. Lee, M. Beirami, R. Ebrahimpour, C. Puyana, M. Tsoukas and K. Avanaki,
Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm,
Skin Res Technol 29(2023), [abstract]
64. S. Mazaheri, M. Zendehdel and A. Haghparast,
Restraint stress potentiates sensitivity to the antinociceptive effect of morphine through orexin receptors in the ventral tegmental area,
Neuropeptides 101(2023), [abstract]
65. M. Mohammadi, K. Eskandari, R. Azizbeigi and A. Haghparast,
The inhibitory effect of cannabidiol on the rewarding properties of methamphetamine in part mediates by interacting with the hippocampal D1-like dopamine receptors,
Prog Neuropsychopharmacol Biol Psychiatry 126(2023), [abstract]
66. P. Navidi, S. Saeedpour , S. Ershadmanesh , M. Miandari Hossein and B. Bahrami ,
Prosocial learning: Model-based or model-free?,
Plos One 18(2023), [abstract]
67. S. Jamali, M. Aliyari Shoorehdeli, M. Daliri and A. Haghparast,
Differential Aspects of Natural and Morphine Reward-related Behaviors in Conditioned Place Preference Paradigm,
Basic and Clinical Neuroscience (2023), [abstract]
68. S. Modaberi, H. Amirteymori, S. Mesgar, k. Eskandari and A. Haghparast,
The blockade of orexin receptors within the dentate gyrus of the hippocampus attenuated methamphetamine-induced reward learning during conditioning place preference,
Pharmacology, Biochemistry and Behavior 226(2023), [abstract]
69. M. Mokari-Mahallati, R. Ebrahimpour, N. Bagheri and H. Karimi-Rouzbahani,
Deeper neural network models better reflect how humans cope with contrast variation in object recognition,
Neuroscience Research 192(2023), [abstract]
70. F. Keyvanfard, A. Rahimi Nasab and A. Nasiraei-Moghaddam,
Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach,
Front Neuroinform 17(2023), [abstract]
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