Seyed-Mahdi Khaligh-Razavi
Affiliations: | Massachusetts Institute of Technology, Cambridge, MA, United States |
Area:
vision, computational neuroscience, memory, fMRI, MEGGoogle:
"Seyed-Mahdi Khaligh-Razavi"Mean distance: 13.64 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentorNikolaus Kriegeskorte | grad student | 2012-2014 | Cambridge |
Gabriel Kreiman | post-doc | 2015- | MIT and Harvard |
Aude Oliva | post-doc | 2015- | MIT |
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Publications
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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, Cichy RM, Pantazis D, et al. (2018) Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain. Journal of Cognitive Neuroscience. 1-18 |
Khaligh-Razavi SM, Henriksson L, Kay K, et al. (2017) Fixed versus mixed RSA:Â 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 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 |