Seyed-Mahdi Khaligh-Razavi

Massachusetts Institute of Technology, Cambridge, MA, United States 
vision, computational neuroscience, memory, fMRI, MEG
"Seyed-Mahdi Khaligh-Razavi"
Mean distance: 13.64 (cluster 29)


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Nikolaus Kriegeskorte grad student 2012-2014 Cambridge
Gabriel Kreiman post-doc 2015- MIT and Harvard
Aude Oliva post-doc 2015- MIT
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Rajaei K, Mohsenzadeh Y, Ebrahimpour R, et al. (2019) Beyond core object recognition: Recurrent processes account for object recognition under occlusion. Plos Computational Biology. 15: e1007001
Khaligh-Razavi SM, Henriksson L, Kay K, et al. (2017) Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models. Journal of Mathematical Psychology. 76: 184-197
Farzmahdi A, Rajaei K, Ghodrati M, et al. (2016) A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans. Scientific Reports. 6: 25025
Wardle SG, Kriegeskorte N, Grootswagers T, et al. (2016) Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG. Neuroimage
Khaligh-Razavi S, Henriksson L, Kay K, et al. (2016) Mixing deep neural network features to explain brain representations Journal of Vision. 16: 369
Khaligh-Razavi SM, Carlin J, Martin Cichy R, et al. (2015) The effects of recurrent dynamics on ventral-stream representational geometry. Journal of Vision. 15: 1089
Henriksson L, Khaligh-Razavi SM, Kay K, et al. (2015) Visual representations are dominated by intrinsic fluctuations correlated between areas. Neuroimage. 114: 275-86
Khaligh-Razavi SM, Kriegeskorte N. (2014) Deep supervised, but not unsupervised, models may explain IT cortical representation. Plos Computational Biology. 10: e1003915
Ghodrati M, Farzmahdi A, Rajaei K, et al. (2014) Feedforward object-vision models only tolerate small image variations compared to human. Frontiers in Computational Neuroscience. 8: 74
Zabbah S, Rajaei K, Mirzaei A, et al. (2014) The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing. Vision Research. 101: 82-93
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