It is my pleasure to invite everyone to attend the next BIC lecture this coming Monday @ 1pm (deGrandpré, MNI);
Dr. Dimitrios Pantazis (MIT) will review his recent work using machine-learning classifiers applied to MEG and fMRI data analysis, with an emphasis on the processes of visual categorization. This lecture is open to everyone as part of the BIC Lecture series. It is also the keynote event of the 4th edition of the MEG@McGill training week. A few seats are still available for the full day of lectures around Dr. Pantazis’ talk on Monday (https://www.mcgill.ca/bic/training-events/training-programs/meg-comprehensive-training).
##Spatiotemporal and representational dynamics of the ventral visual pathway revealed by MEG
Abstract:
The human brain can rapidly and effortlessly recognize complex visual information within only a couple hundred milliseconds. To understand this remarkable behavior, we use MEG sensors to measure neuronal signals as the brain transforms low level visual information into semantic content. In this talk I will offer novel insights in the duration and sequencing of visual cognitive processes obtained through multivariate analysis methods and MEG/fMRI data fusion. I will 1) demonstrate a clear dissociation between feedforward and feedback early visual processes, with well-defined temporal signatures for both mechanisms; 2) determine a lower boundary in the visual hierarchy for memory-related neural signals; and 3) characterize the orientation selectivity of gamma induced responses and provide a principled approach to link gamma responses to the perceptual Gestalt.
Dimitrios Pantazis, who joined the McGovern Institute at MIT in 2010, is the director of the Magnetoencephalography Laboratory within the Martinos Imaging Center at MIT. He was previously a research assistant professor at the University of Southern California from 2008-2010. His research focuses on the development of novel MEG methods to holistically capture spatiotemporal brain activation and the study of visual brain representations.
Similarity-Based Fusion of MEG and fMRI Reveals Spatio-Temporal Dynamics in Human Cortex During Visual Object Recognition. Cichy RM, Pantazis D, Oliva A. Cereb Cortex. 2016 Aug;26(8):3563-79. doi: 10.1093/cercor/bhw135. Epub 2016 May 27.
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A.
Sci Rep. 2016 Jun 10;6:27755. doi: 10.1038/srep27755.
Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans? Cichy RM, Ramirez FM, Pantazis D. Neuroimage. 2015 Nov 1;121:193-204. doi: 10.1016/j.neuroimage.2015.07.011. Epub 2015 Jul 8.
Resolving human object recognition in space and time. Cichy RM, Pantazis D, Oliva A. Nat Neurosci. 2014 Mar;17(3):455-62. doi: 10.1038/nn.3635. Epub 2014 Jan 26.