Top-down Modulation in Human Visual Cortex │Dr Mohamed Abdelhack

Summary:
Human vision flaunts a remarkable ability to recognize objects in the surrounding environment even in the absence of complete visual representation of these objects. This process is done almost intuitively and it was not until scientists had to tackle this problem in computer vision that they noticed its complexity. While current advances in artificial vision systems have made great strides exceeding human level in normal vision tasks, it has yet to achieve a similar robustness level. One cause of this robustness is the extensive connectivity that is not limited to a feedforward hierarchical pathway similar to the current state-of-the-art deep convolutional neural networks, but also comprises recurrent and top-down connections. They allow the human brain to enhance the neural representations of degraded images in concordance with meaningful representations stored in memory. The mechanisms by which these different pathways interact are still not understood.
In this seminar, studies concerning the effect of recurrent and top-down modulation on the neural representations resulting from viewing blurred images will be presented. Those studies attempted to uncover the role of recurrent and top-down connections in human vision. The results presented challenge the notion of predictive coding as a mechanism for top-down modulation of visual information during natural vision. They show that neural representation enhancement (sharpening) appears to be a more dominant process of different levels of visual hierarchy. They also show that inference in visual recognition is achieved through a Bayesian process between incoming visual information and priors from deeper processing regions in the brain.
Speaker Bio:
Originating from Alexandria Egypt, Mohamed developed his interest in the human brain while studying electrical engineering at Alexandria University in Egypt. He started venturing into the field by working on Brain-Computer Interfacing first as an intern in the National Center for Adaptive Neurotechnologies with Prof. Jonathan Wolpaw and then in his graduation thesis with Dr Hania Farag. He then moved to Japan for his graduate studies where he investigated several subfields of neuroscience conducting structural analysis of neurons with Dr Hermina Nedelescu and studying neural substrates of locomotion in C. elegans with Prof. Ichiro Maruyama. He eventually settled in the field of cognitive and computational neuroscience studying top-down modulation in the human visual cortex and modelling this process using deep neural networks with Prof. Yukiyasu Kamitani in Kyoto University where he obtained his PhD degree in 2019. He is currently working as a postdoctoral research associate at Washington University in St. Louis attempting to apply his intuition from human neuroscience into artificial intelligence and applying it to healthcare applications. His current major project is building a clinical decision support system that is to be deployed to Barnes Jewish Hospital (TECTONICS project). He describes his research interests as closing the gap between human and artificial intelligence. He is also enthusiastic about collaborating with several partners in Africa and the Middle East to use science to solve problems that are endemic to the region and help students reach scientific knowledge.
Coordinator:
Mosab Ali Awadelkareem, Al-Neelain University, Sudan.
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