[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit ::
:: 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:   (119 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]   (32 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience
References
1. Güçlü U, van Gerven MA. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J Neurosci. 2015; 35(27): 10005-14. [DOI:10.1523/JNEUROSCI.5023-14.2015]
2. Dumoulin SO, Wandell BA. Population receptive field estimates in human visual cortex. Neuroimage. 2008; 39(2): 647-60. [DOI:10.1016/j.neuroimage.2007.09.034]
3. Kay KN, Naselaris T, Prenger RJ, Gallant JL. Identifying natural images from human brain activity. Nature. 2008; 452(7185): 352-5. [DOI:10.1038/nature06713]
4. Nishimoto S, Vu AT, Naselaris T, Benjamini Y, Yu B, Gallant JL. Reconstructing visual experiences from brain activity evoked by natural movies. Curr Biol. 2011; 21(19): 1641-6. [DOI:10.1016/j.cub.2011.08.031]
5. Naselaris T, Stansbury DE, Gallant JL. Cortical representation of animate and inanimate objects in complex natural scenes. J Physiol Paris. 2012; 106(5-6): 239-49. [DOI:10.1016/j.jphysparis.2012.02.001]
6. Stansbury DE, Naselaris T, Gallant JL. Natural scene statistics account for the representation of scene categories in human visual cortex. Neuron. 2013; 79(5): 1025-34. [DOI:10.1016/j.neuron.2013.06.034]
7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553): 436. [DOI:10.1038/nature14539]
8. Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A. Deep neural networks predict hierarchical spatio-temporal cortical dynamics of human visual object recognition. arXiv preprint arXiv:1601.02970. 2016. [DOI:10.1038/srep27755]
9. Agrawal P, Stansbury D, Malik J, Gallant JL. Pixels to voxels: modeling visual representation in the human brain. arXiv preprint arXiv:1407.5104. 2014.
10. Khaligh-Razavi SM, Kriegeskorte N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput Biol. 2014; 10(11): e1003915. doi: 10.1371/journal.pcbi.1003915. [DOI:10.1371/journal.pcbi.1003915]
11. St-Yves G, Naselaris T. The feature-weighted receptive field: an interpretable encoding model for complex feature spaces. NeuroImage. 2018; 180: 188-202. [DOI:10.1016/j.neuroimage.2017.06.035]
12. Wen H, Shi J, Zhang Y, Lu KH, Cao J, Liu Z. Neural encoding and decoding with deep learning for dynamic natural vision. Cerebral Cortex. 2017; 28(12): 4136-60. [DOI:10.1093/cercor/bhx268]
13. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012; 25(2): 1-9.
14. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision. 2015; 115(3): 211-52. [DOI:10.1007/s11263-015-0816-y]
15. Henson R, Friston K. Convolution models for fMRI. statistical parametric mapping: The analysis of functional brain images. 2007: 178-92. [DOI:10.1016/B978-012372560-8/50014-0]



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

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
URL: http://shefayekhatam.ir/article-1-1991-en.html


Volume 7, Issue 4 (autumn 2019) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
Persian site map - English site map - Created in 0.06 seconds with 32 queries by YEKTAWEB 3991