“Papers of School of Cognitive Sciences”

 

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  491. M. Nouri, A. Rezayof, A. Haeri-Rohani , M.R. Zarrindast and S. Ahmadi,
Nicotine improves morphine-induced impairment of memory: possible involvement of N-methyl-D-aspartate receptors in the nucleus accumbens,
Dev Neurobiol 67(2007), 1118-27  [abstract]

   492. A. Khalilzadeh, M. Sattari-Naeini and M.R. Zarrindast,
Involvement of glucose and ATP-sensitive potassium (K+) channels on morphine-induced conditioned place preference,
EUR J PHARMACOL 573(2007), 133-8  [abstract]

   493. N. Malekmohamadi, P. Heidari, M. Sahebgharani and M.R. Zarrindast,
Effects of Clozapine and Sulpiride on Morphine State-Dependent Memory in the Step-Down Passive Avoidance Test,
PHARMACOLOGY 79(2007), 149-53  [abstract]

   494. A. Rezayof, F. Nazari-Serenjeh, M.R. Zarrindast, H. Sepehri and L. Delphi,
Morphine-induced place preference: Involvement of cholinergic receptors of the ventral tegmental area,
EUR J PHARMACOL 562(2007), 92-102  [abstract]

   495. M. Avanaki and R. Ebrahimpour,
In-service video quality measurements in optical fiber links based on neural network,
Neural Network World 5(2007), 457-468  [abstract]

   496. S. Haidarian Shahri and M. Ahmadabadi,
A bayesian approach to attention control and concept abstraction,
Lecture Notes in Artificial Intelligent 4840(2007), 155?170  [abstract]

   497. H. Mobahi, M. Ahmadabadi and B. Araabi,
A biologically inspired method for conceptual imitation using reinforcement learning,
Applied Artificial Intelligence 21(2007), 155 - 183  [abstract]

   498. H. Shariatpanahi and M. Ahmadabadi,
biologically inspired framework for learning and abstract representation of attention control,
Lecture Notes in Artificial Intelligent 4840(2007), 307-324  [abstract]

   499. T. Babaie, R. Karimizandi and C. Lucas,
Prediction of solar conditions by emotional learning,
Intelligent Data Analysis 10(2007), 583-597  [abstract]

   500. A. Gholipour, C. Lucas , BN. Araabi , M. Mirmomeni and M. Shafiee,
Extracting the main patterns of natural time series for long-term neurofuzzy prediction,
Neural Computing and Applications 16(2007), 383-393  [abstract]

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