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Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
We propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. Expand
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Information-based functional brain mapping.
The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. Expand
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Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species.Expand
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Circular analysis in systems neuroscience: the dangers of double dipping
A neuroscientific experiment typically generates a large amount of data, of which only a small fraction is analyzed in detail and presented in a publication. However, selection among noisyExpand
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A Toolbox for Representational Similarity Analysis
We introduce a toolbox for representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. Expand
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Representational geometry: integrating cognition, computation, and the brain
Highlights • Representational geometry is a framework that enables us to relate brain, computation, and cognition.• Representations in brains and models can be characterized by representationalExpand
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Individual faces elicit distinct response patterns in human anterior temporal cortex
Visual face identification requires distinguishing between thousands of faces we know. This computational feat involves a network of brain regions including the fusiform face area (FFA) and anteriorExpand
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Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
We investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. Expand
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Representational dynamics of object vision: the first 1000 ms.
Human object recognition is remarkably efficient. In recent years, significant advancements have been made in our understanding of how the brain represents visual objects and organizes them intoExpand
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Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.
  • N. Kriegeskorte
  • Computer Science, Medicine
  • Annual review of vision science
  • 18 November 2015
Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Expand
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