Receptive Field Encoding Model for Dynamic Natural Vision

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KAMALI FATEMEH; Suratgar Amir Abolfazl; Menhaj Mohamad Bagher; Abassi Asl Reza

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Journal: Neuroscience Journal of Shefaye Khatam Year:2019 | Volume:7 | Issue:4 Page(s): 1-7

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Title

Receptive Field Encoding Model for Dynamic Natural Vision

Author(s)

KAMALI FATEMEH | Suratgar Amir Abolfazl | Menhaj Mohamad Bagher | Abassi Asl Reza | Issue Writer Certificate

Keywords

Magnetic Resonance ImagingQ1

Visual CortexQ1

BrainQ2

Abstract

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

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APA: Copy

KAMALI, FATEMEH, Suratgar, Amir Abolfazl, Menhaj, Mohamad Bagher, & Abassi Asl, Reza. (2019). Receptive Field Encoding Model for Dynamic Natural Vision. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, 7(4 ), 1-7. SID. https://sid.ir/paper/257953/en

Vancouver: Copy

KAMALI FATEMEH, Suratgar Amir Abolfazl, Menhaj Mohamad Bagher, Abassi Asl Reza. Receptive Field Encoding Model for Dynamic Natural Vision. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM[Internet]. 2019;7(4 ):1-7. Available from: https://sid.ir/paper/257953/en

IEEE: Copy

FATEMEH KAMALI, Amir Abolfazl Suratgar, Mohamad Bagher Menhaj, and Reza Abassi Asl, “Receptive Field Encoding Model for Dynamic Natural Vision,” NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, vol. 7, no. 4 , pp. 1–7, 2019, [Online]. Available: https://sid.ir/paper/257953/en

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