Peeling the Onion of Brain Representations.
暂无分享,去创建一个
[1] William T. Newsome,et al. Cortical microstimulation influences perceptual judgements of motion direction , 1990, Nature.
[2] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[3] A. Dale,et al. From retinotopy to recognition: fMRI in human visual cortex , 1998, Trends in Cognitive Sciences.
[4] S. Edelman,et al. Toward direct visualization of the internal shape representation space by fMRI , 1998, Psychobiology.
[5] S Edelman,et al. Representation is representation of similarities , 1996, Behavioral and Brain Sciences.
[6] William Bechtel,et al. Representations and Cognitive Explanations: Assessing the Dynamicist's Challenge in Cognitive Science , 1998, Cogn. Sci..
[7] T. Gelder,et al. The dynamical hypothesis in cognitive science , 1998, Behavioral and Brain Sciences.
[8] Peter Dayan,et al. The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.
[9] A. Zador,et al. Neural representation and the cortical code. , 2000, Annual review of neuroscience.
[10] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[11] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[12] R. Goebel,et al. Mirror-Symmetric Tonotopic Maps in Human Primary Auditory Cortex , 2003, Neuron.
[13] T. Carlson,et al. Patterns of Activity in the Categorical Representations of Objects , 2003, Journal of Cognitive Neuroscience.
[14] David D. Cox,et al. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.
[15] Bruno A Olshausen,et al. Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.
[16] D. Chklovskii,et al. Maps in the brain: what can we learn from them? , 2004, Annual review of neuroscience.
[17] F. Tong,et al. Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.
[18] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[19] K. Grill-Spector,et al. Repetition and the brain: neural models of stimulus-specific effects , 2006, Trends in Cognitive Sciences.
[20] J. Gallant,et al. Complete functional characterization of sensory neurons by system identification. , 2006, Annual review of neuroscience.
[21] R. Kiani,et al. Microstimulation of inferotemporal cortex influences face categorization , 2006, Nature.
[22] D. Chklovskii,et al. Wiring optimization can relate neuronal structure and function. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[23] Rainer Goebel,et al. Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[24] A. Pouget,et al. Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.
[25] Nikolaus Kriegeskorte,et al. Analyzing for information, not activation, to exploit high-resolution fMRI , 2007, NeuroImage.
[26] R. Goebel,et al. Individual faces elicit distinct response patterns in human anterior temporal cortex , 2007, Proceedings of the National Academy of Sciences.
[27] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[28] Liam Paninski,et al. Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.
[29] J. Gallant,et al. Identifying natural images from human brain activity , 2008, Nature.
[30] John P. Cunningham,et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.
[31] Brian A. Wandell,et al. Population receptive field estimates in human visual cortex , 2008, NeuroImage.
[32] Tom Michael Mitchell,et al. Predicting Human Brain Activity Associated with the Meanings of Nouns , 2008, Science.
[33] Nikolaus Kriegeskorte,et al. Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience , 2008, Frontiers in systems neuroscience.
[34] Keiji Tanaka,et al. Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.
[35] Tom M. Mitchell,et al. Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.
[36] N. Kriegeskorte,et al. Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.
[37] Nikolaus Kriegeskorte,et al. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI , 2010, NeuroImage.
[38] Jack L. Gallant,et al. Encoding and decoding in fMRI , 2011, NeuroImage.
[39] Nikolaus Kriegeskorte,et al. Pattern-information analysis: From stimulus decoding to computational-model testing , 2011, NeuroImage.
[40] Karl J. Friston,et al. Comparing the similarity and spatial structure of neural representations: A pattern-component model , 2011, NeuroImage.
[41] J. Haynes. Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .
[42] H. Sompolinsky,et al. Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. , 2012, Annual review of neuroscience.
[43] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[44] Michael S. Pratte,et al. Decoding patterns of human brain activity. , 2012, Annual review of psychology.
[45] K. Grill-Spector,et al. Electrical Stimulation of Human Fusiform Face-Selective Regions Distorts Face Perception , 2012, The Journal of Neuroscience.
[46] Jack L. Gallant,et al. A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain , 2012, Neuron.
[47] D. Poeppel. The maps problem and the mapping problem: Two challenges for a cognitive neuroscience of speech and language , 2012, Cognitive neuropsychology.
[48] Matthew T. Kaufman,et al. Neural population dynamics during reaching , 2012, Nature.
[49] E. Marder,et al. From the connectome to brain function , 2013, Nature Methods.
[50] M. Sahani,et al. Cortical control of arm movements: a dynamical systems perspective. , 2013, Annual review of neuroscience.
[51] N. Kriegeskorte,et al. Representational geometry: integrating cognition, computation, and the brain , 2013, Trends in Cognitive Sciences.
[52] Radoslaw Martin Cichy,et al. Resolving human object recognition in space and time , 2014, Nature Neuroscience.
[53] J. S. Guntupalli,et al. Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.
[54] A. Pouget,et al. Information-limiting correlations , 2014, Nature Neuroscience.
[55] Li Su,et al. A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..
[56] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[57] S. Dehaene,et al. Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.
[58] John-Dylan Haynes,et al. Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA , 2014, NeuroImage.
[59] J. Diedrichsen,et al. Hand use predicts the structure of representations in sensorimotor cortex , 2015, Nature Neuroscience.
[60] Thomas Naselaris,et al. Resolving Ambiguities of MVPA Using Explicit Models of Representation , 2015, Trends in Cognitive Sciences.
[61] J. Haynes. A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.
[62] Stuart P. Wilson,et al. What, if anything, are topological maps for? , 2015, Developmental neurobiology.
[63] Nikolaus Kriegeskorte,et al. Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.
[64] Josh H. McDermott,et al. Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition , 2015, Neuron.
[65] Marcel A. J. van Gerven,et al. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.
[66] Surya Ganguli,et al. On simplicity and complexity in the brave new world of large-scale neuroscience , 2015, Current Opinion in Neurobiology.
[67] Ha Hong,et al. Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.
[68] Thomas L. Griffiths,et al. Supplementary Information for Natural Speech Reveals the Semantic Maps That Tile Human Cerebral Cortex , 2022 .
[69] Jörn Diedrichsen,et al. Reliability of dissimilarity measures for multi-voxel pattern analysis , 2016, NeuroImage.
[70] J. Diedrichsen,et al. On the distribution of cross-validated Mahalanobis distances , 2016, 1607.01371.
[71] Naoshige Uchida,et al. Demixed principal component analysis of neural population data , 2016, eLife.
[72] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[73] Jörn Diedrichsen,et al. Inferring brain-computational mechanisms with models of activity measurements , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.
[74] Jonathan W. Pillow,et al. A Bayesian method for reducing bias in neural representational similarity analysis , 2016, bioRxiv.
[75] Marcel A. J. van Gerven,et al. A primer on encoding models in sensory neuroscience , 2017 .
[76] Jörn Diedrichsen,et al. Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis , 2017, bioRxiv.
[77] Andres Hoyos Idrobo,et al. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines , 2016, NeuroImage.
[78] Jörn Diedrichsen,et al. Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns , 2017, NeuroImage.
[79] L .Paninski,et al. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience , 2017, Current Opinion in Neurobiology.
[80] Nikolaus Kriegeskorte,et al. Cognitive computational neuroscience , 2018, Nature Neuroscience.
[81] Chris I. Baker,et al. Deconstructing multivariate decoding for the study of brain function , 2017, NeuroImage.
[82] J. Diedrichsen. Representational models and the feature fallacy , 2018 .