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]
|