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Deep supervised, but not unsupervised, models may explain IT cortical representationHow can selection of biologically inspired features improve the performance of a robust object recognition model?Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role.Feedforward object-vision models only tolerate small image variations compared to human.Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG.A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans.Visual representations are dominated by intrinsic fluctuations correlated between areas.The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing.Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive PerformanceA self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)
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P50
description
researcher ORCID ID = 0000-0002-5700-1704
@en
wetenschapper
@nl
name
Seyed-Mahdi Khaligh-Razavi
@ast
Seyed-Mahdi Khaligh-Razavi
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Seyed-Mahdi Khaligh-Razavi
@es
Seyed-Mahdi Khaligh-Razavi
@nl
type
label
Seyed-Mahdi Khaligh-Razavi
@ast
Seyed-Mahdi Khaligh-Razavi
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Seyed-Mahdi Khaligh-Razavi
@es
Seyed-Mahdi Khaligh-Razavi
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prefLabel
Seyed-Mahdi Khaligh-Razavi
@ast
Seyed-Mahdi Khaligh-Razavi
@en
Seyed-Mahdi Khaligh-Razavi
@es
Seyed-Mahdi Khaligh-Razavi
@nl
P106
P1153
55037384600
P31
P496
0000-0002-5700-1704