1. Natural Image Reconstruction From fMRI Using Deep Learning: A Survey
2. Nicolas Pinto , David Doukhan , James J DiCarlo , and David D Cox . A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS computational biology, 5(11):e1000579 , 2009 . Nicolas Pinto, David Doukhan, James J DiCarlo, and David D Cox. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS computational biology, 5(11):e1000579, 2009.
3. Alex Krizhevsky , Geoffrey Hinton , Learning multiple layers of features from tiny images . 2009 . Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
4. Martin Schrimpf , Jonas Kubilius , Ha Hong , Najib J Majaj , Rishi Rajalingham , Elias B Issa , Kohitij Kar , Pouya Bashivan , Jonathan Prescott-Roy , Franziska Geiger , et al. Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, page 407007 , 2018 . Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J Majaj, Rishi Rajalingham, Elias B Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, et al. Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, page 407007, 2018.
5. Guohua Shen , Tomoyasu Horikawa , Kei Majima , and Yukiyasu Kamitani . Deep image reconstruction from human brain activity. PLoS computational biology, 15(1):e1006633 , 2019 . Guohua Shen, Tomoyasu Horikawa, Kei Majima, and Yukiyasu Kamitani. Deep image reconstruction from human brain activity. PLoS computational biology, 15(1):e1006633, 2019.