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:: Volume 7, Issue 4 (Autumn 2019) ::
Shefaye Khatam 2019, 7(4): 1-7 Back to browse issues page
Receptive Field Encoding Model for Dynamic Natural Vision
Fatemeh Kamali , Amir Abolfazl Suratgar * , Mohamad Bagher Menhaj , Reza Abbasi Asl
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran , a-suratgar@aut.ac.i
Abstract:   (3612 Views)
Introduction: Encoding models are used to predict human brain activity in response to sensory stimuli. The purpose of these models is to explain how sensory information represent in the brain. Convolutional neural networks trained by images are capable of encoding magnetic resonance imaging data of humans viewing natural images. Considering the hemodynamic response function, these networks are capable of estimating the blood oxygen level dependence of subject viewing videos without any recurrence or feedback mechanism. For this purpose, feature map extracted from the convolutional neural network and the concept of receptive field has been used for the encoding model. The main assumption of this model is that activity in each voxel encodes a spatially localized region across multiple feature maps and for each voxel and this area are fixed for all feature maps. Contribution of each feature map in the activity of each voxel is determined by the corresponding weight. Materials and Methods: In this study, three healthy volunteers watching a set of videos. This collection contains images that represent real-life visual experience. MRI and fMRI data are acquired on a 3 tesla MRI system phase-array surface coil. Results: Data revealed that human visual cortex has hierarchical structure. Earlier visual areas have a smaller receptive field size in and response to simple feature like edge, whereas higher visual areas have a larger receptive field size and response to more complex features, such as pattern. Conclusion: This model of video stimuli has a higher interpretation capacity than the previous models.
Keywords: Magnetic Resonance Imaging, Visual Cortex, Brain
Full-Text [PDF 879 kb]   (1386 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience
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Kamali F, Suratgar A A, Menhaj M B, Abbasi Asl R. Receptive Field Encoding Model for Dynamic Natural Vision. Shefaye Khatam 2019; 7 (4) :1-7
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 4 (Autumn 2019) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
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