Identifying natural images from human brain activity

By Kendrick Norris Kay, Thomas Naselaris, Ryan J. Prenger, Jack L. Gallant. Nature, 2008

1,451 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/12ec492b6b2ad7c2cc47a5612e8d2a9b7fb35f91

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In Cognitive Neuroscience

Citations in the corpus

Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “Meanwhile, another new approach showed that multivoxel responses to new stimuli could be predicted based on highdimensional feature models of stimuli (Kay et al., 2008; Mitchell et al., 2008; Naselaris et al., 2009; Nishimoto et al., 2011).”
Reconstructing Speech from Human Auditory Cortex (2012, methodology): “An important application of this approach has also been demonstrated in the study of visual object recognition in the primate visual system [36,49,50].”
Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, methodology): “…for comparing internal representations in neural population codes between models and brains enable us to test models as theories of brain information processing (Dumoulin & Wandell 2008; Kay et al. 2008; Kriegeskorte et al. 2008a,b, 2013; Kriegeskorte 2011; Mitchell et al. 2008; Nili et al. 2014).”
Representational dynamics of object vision: the first 1000 ms. (2013, background): “(2008) and Kiani et al. (2007), and in our own study, was animacy.”
A continuous semantic space describes the representation of thousands of object and action categories across the human brain (2012, methodology): “A regularized, linearized ﬁnite impulse response regression model was then estimated for each cortical voxel recorded in each subject’s brain (Kay et al., 2008; Mitchell et al., 2008; Naselaris et al., 2009; Nishimoto et al., 2011).”
Decoding brain states from fMRI connectivity graphs (2011, background): “…on a classi ﬁ er to predict the subject's brain state from the BOLD responses in a set of selected voxels, such as visual (Cox and Savoy, 2003; Haynes and Rees, 2005; Kamitani and Tong, 2005; Thirion et al., 2006; Kay et al., 2008; Miyawaki et al., 2008) or auditory cortices (Ethofer et al., 2009).”
Inferring brain-computational mechanisms with models of activity measurements (2016, background): “B 371: 20160278. http://dx.doi.org/10.1098/rstb.2016.0278 Accepted: 19 July 2016 One contribution of 15 to a Theo Murphy meeting issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “Therefore they can be used to accumulate subjects’ responses for rich decoding studies (Kay et al., 2008).”
The brain–computer interface cycle (2009, neutral): “state [117–119].”
Seeing it all: Convolutional network layers map the function of the human visual system (2017, methodology): “272 Note that retinotopic mapping was also used in the original study [17] to validate 273 the forward model estimated using Gabor filters.”
Reconstructing visual experiences from brain activity evoked by natural movies (2011, neutral): “Identification accuracy [7, 19] measures how well a model can correctly associate an observed BOLD signal pattern with the specific stimulus that evoked it.”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, background): “Increasingly subtle methods have allowed classification of stimuli subconsciously perceived by subjects (Haynes and Rees 2005a); classification of novel stimuli spatially or thematically related to previously viewed stimuli (Kay et al. 2008; Walther et al. 2009); classification of attentional shifts”
The code for facial identity in the primate brain (2017, methodology): “If this is true, then by simply inverting this equation, we should be able to linearly decode the facial features from the population response (Cowen et al., 2014; Kay et al., 2008; Nestor et al., 2016).”
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014, methodology): “This result is a signiﬁcant improvement on the earlier results in the literature where low-level features were used [11, 15], suggesting that mid-and high-level features are also important for identiﬁcation.”