Decoding the visual and subjective contents of the human brain

By Yukiyasu Kamitani, Frank Tong. Nature Neuroscience, 2005

2,023 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/200a65b129ac7addce69470af666ed9b48b0c2da

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In Cognitive Neuroscience

Citations in the corpus

How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration (2007, background): “Recent techniques in fMRI that examine patterns of activation (Beauchamp et al., 2004; Haxby et al., 2001; Haynes and Rees, 2005; Kamitani and Tong, 2005; Kriegeskorte et al., 2006) should also benefit from reliable high-resolution single subject activation maps.”
Deconstructing multivariate decoding for the study of brain function (2017, methodology): “Part of the appeal of decoding came from the idea that decodingmay have access to fine-scale information beyond the resolution of fMRI (Kamitani and Tong, 2005; but see Freeman et al., 2011; Op de Beeck, 2010) and the possibility to describe these methods as tools for “mind-reading” (Haynes”
Introduction to machine learning for brain imaging (2011, methodology): “…analysis tools that correlate single voxels with behavior to a full multivariate correlation analysis (e.g., Hansen et al., 1999; Haxby et al., 2001; Strother et al., 2002; Cox and Savoy, 2003; Kamitani and Tong, 2005; Haynes and Rees, 2006; Kriegeskorte et al., 2006; Mitchell et al., 2008).”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “(C) This map shows a flattened representation of V1 where the orientation eliciting the strongest response is color coded (see inset) for each voxel (Kamitani and Tong, 2005).”
Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex (2012, positive): “First, in line with earlier reports (Jehee et al., 2011; Kamitani and Tong, 2005), we found that task relevance enhanced orientation classification accuracy: accuracy was overall higher during the orientation task than during the contrast task (F1,17 = 8.”
Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System (2015, background): “Consistent with this, multiple fMRI studies have demonstrated that distributed response patterns within a brain region contain a rich amount of information above and beyond what is present in univariate modulation (Kamitani and Tong, 2005; Kriegeskorte and Bandettini, 2007; Çukur et al., 2013).”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, positive): “Although careful examination is necessary to determine whether the voxels represent the decoded task parameter or some other correlated variable (Kamitani and Tong, 2005), comparisons of decoding performance for different brain areas can provide a powerful method for mapping the information availabl”
Neural Decoding of Visual Imagery During Sleep (2013, methodology): “For the stimulus-induced dataset, samples from one run were left for testing, and the rest were used for decoder training (repeated until all runs were tested; leave-one-run-out cross validation) (5).”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, background): “This is one of the most fundamental questions in neuro-science research and the answer to this question, in multiple contexts, has provided crucial knowledge of how information is processed in the brain (see for example Haxby et al. 2001 and Kamitani and Tong 2005).”
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision (2016, methodology): “These questions, concerning neural encoding and decoding (Naselaris et al. 2011), have been mostly addressed with static or arti ﬁ cial stimuli (Kamitani and Tong 2005; Haynes and Rees 2006).”