Deep image reconstruction from human brain activity
By Guohua Shen, Tomoyasu Horikawa, Kei Majima, Yukiyasu Kamitani. bioRxiv, 2017
359 citations recorded by the source corpus.
Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases (Miyawaki et al., 2008; Wen et al., 2016) or to the matching to exemplars (Naselaris et al., 2009; Nishimoto et al., 2011). Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features (Horikawa & Kamitani, 2017). Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed ‘reconstructs’ or ‘generates’ images from brain activity, not simply matches to exemplars. A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/29311a3c64af1f2d87e5f89ff62ad923fc50ee8a
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In Cognitive Neuroscience
Citations in the corpus
- Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, background): “In a related vein, studies have also used CNNs to decode neural activity in order to recreate the stimuli that are being presented to subjects [48].”
- Accelerating the discovery of materials for clean energy in the era of smart automation (2018, methodology): “In late 2017, a DNN was successfully shown to enable the reconstruction of perceptual and subjective images from the activity of human brains 38 .”