تصویر

Seyed Mahdi Khaligh-Razavi    Ph.D
Stem Cells and Developmental Biology
Assistant professor

Email:seyed@cognetivity.com
Tel:+98 21 23562512
Fax:
CV:-

Dr. Khaligh-Razavi is a neuroscientist and entrepreneur; co-founder of Cognetivity ltd, UK; and assistant professor at Royan Institute, Brain and Cognitive Sciences Dept. With a background in computer science and machine learning, he completed his PhD in 2014 at the MRC Cognition and Brain Sciences Unit, Cambridge University, studying visual object recognition in human and machine. After that he became a postdoctoral researcher at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), studying the human brain using multimodal neuroimaging techniques and computational modelling. During this time, he was also affiliated with Harvard Catalyst (The Harvard University Clinical and Translational Science Centre), where he has worked on medical device development.

Research in our lab is multidisciplinary and translational. It involves developing methods to better study the brain, applying these methods to understand how the brain works, and taking advantage of this knowledge to develop healthcare products where we can have a real impact by improving people’s lives. We study the healthy human brain, and the brain under a few neurological conditions, such as dementia and multiple sclerosis. We use a variety of tools and techniques in our research: Brain imaging: fMRI: functional magnetic resonance imaging EEG: Electroencephalogram MEG: Magnetoencephalography Computational models (e.g. convolutional neural nets) Machine learning Artificial intelligence
The neural separation and integration of object and background scene information in natural images C Mullin, SM Khaligh-Razavi, D Pantazis, A Oliva Journal of Vision 17 (10), 1089-1089 2017 Combining human MEG and fMRI data reveals the spatio-temporal dynamics of animacy and real-world object size SM Khaligh-Razavi, R Cichy, D Pantazis, A Oliva Journal of Vision 17 (10), 574-574 2017 Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role M Ghodrati, SM Khaligh-Razavi, SR Lehky Progress in neurobiology 156, 214-255 2017 Tracking the spatiotemporal neural dynamics of object properties in the human brain SM Khaligh-Razavi, RM Cichy, D Pantazis, A Oliva Cognitive Computational Neuroscience 2017 Content-Dependent Fusion: Combining Human MEG and FMRI Data to Reveal Spatiotemporal Dynamics of Animacy and Real-world Object Size SM Khaligh-Razavi, RM Cichy, D Pantazis, A Oliva AAAI Publications 2017 Sudden emergence of categoricality at the lateral-occipital stage of ventral visual processing A Walther, J Diedrichsen, M Mur, SM Khaligh-Razavi, N Kriegeskorte Journal of Vision 16 (12), 407-407 2016 Temporal Dynamics of Memorability: An Intrinsic Brain Signal Distinct from Memory SM Khaligh-Razavi, WA Bainbridge, D Pantazis, A Oliva Journal of Vision 16 (12), 38-38 2016 Mixing deep neural network features to explain brain representations SM Khaligh-Razavi, L Henriksson, K Kay, N Kriegeskorte Journal of Vision 16 (12), 369-369 2016 Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG SG Wardle, N Kriegeskorte, T Grootswagers, SM Khaligh-Razavi, … Neuroimage 132, 59-70 2016 A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans A Farzmahdi, K Rajaei, M Ghodrati, R Ebrahimpour, SM Khaligh-Razavi Scientific reports 6, 25025 2016 Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models SM Khaligh-Razavi, L Henriksson, K Kay, N Kriegeskorte Journal of Mathematical Psychology 2016 From what we perceive to what we remember: Characterizing representational dynamics of visual memorability SM Khaligh-Razavi, WA Bainbridge, D Pantazis, A Oliva bioRxiv, 049700 2016 The effects of recurrent dynamics on ventral-stream representational geometry SM Khaligh-Razavi, J Carlin, RM Cichy, N Kriegeskorte Journal of vision 15 (12), 1089-1089 2015 Visual representations are dominated by intrinsic fluctuations correlated between areas L Henriksson, SM Khaligh-Razavi, K Kay, N Kriegeskorte NeuroImage 114, 275-286 2015 Perceptual similarity of visual patterns predicts the similarity of their dynamic neural activation patterns measured with MEG SG Wardle, N Kriegeskorte, T Grootswagers, SM Khaligh-Razavi, … arXiv preprint arXiv:1506.02208 2015 Representational geometries of object vision in man and machine SM Khaligh-Razavi University of Cambridge 2015 System for assessing a mental health disorder SM KHALIGH-RAZAVI, S HABIBI GB Patent WO2015067945 A1 2015 Deep supervised, but not unsupervised, models may explain IT cortical representation SM Khaligh-Razavi, N Kriegeskorte PLoS computational biology 10 (11), e1003915 2014 What is the nature of the decodable neuromagnetic signal? MEG, Models, and Perception. T Carlson, S Khaligh-Razavi, N Kriegeskorte Journal of Vision 14 (10), 585-585 2014 The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing S Zabbah, K Rajaei, A Mirzaei, R Ebrahimpour, SM Khaligh-Razavi Vision research 101, 82-93 2014 Feedforward object-vision models only tolerate small image variations compared to human M Ghodrati, A Farzmahdi, K Rajaei, R Ebrahimpour, SM Khaligh-Razavi Frontiers in computational neuroscience 8, 74 2014 What you need to know about the state-of-the-art computational models of object-vision: A tour through the models SM Khaligh-Razavi arXiv preprint arXiv:1407.2776 2014 Explaining the hierarchy of visual representational geometries by remixing of features from many computational vision models SM Khaligh-Razavi, L Henriksson, K Kay, N Kriegeskorte bioRxiv, 009936 2014 Intrinsic cortical dynamics dominate population responses to natural images across human visual cortex L Henriksson, SM Khaligh-Razavi, K Kay, N Kriegeskorte bioRxiv, 008961 2014 Population-code representations of natural images across human visual areas L Henriksson, SM Khaligh-Razavi, N Kriegeskorte Journal of Vision 13 (9), 1035-1035 2013 Predicting the Human Reaction Time Based On Natural Image Statistics in a Rapid Categorization Task A Mirzaei, SM Khaligh-Razavi, M Ghodrati, S Zabbah, R Ebrahimpour Vision Research 2013 Object-vision models that better explain IT also categorize better, but all models fail at both SM Khaligh-Razavi, N Kriegeskorte Cosyne Abstracts, Salt Lake City USA 2013 A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization K Rajaei, SM Khaligh-Razavi, M Ghodrati, R Ebrahimpour, MESA Abadi PloS one 7 (6), e38478 2012 How can selection of biologically inspired features improve the performance of a robust object recognition model? M Ghodrati, SM Khaligh-Razavi, R Ebrahimpour, K Rajaei, M Pooyan PloS one 7 (2), e32357