• Publications
  • Influence
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
TLDR
A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.
Information-based functional brain mapping.
TLDR
The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest.
Circular analysis in systems neuroscience: the dangers of double dipping
TLDR
It is argued that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection, and 'double dipping' the use of the same dataset for selection and selective analysis is suggested.
A Toolbox for Representational Similarity Analysis
TLDR
A Matlab toolbox for representational similarity analysis is introduced, designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques.
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.
  • N. Kriegeskorte
  • Biology, Computer Science
    Annual review of vision science
  • 18 November 2015
TLDR
This work states that biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision, are entering an exciting new era.
Individual faces elicit distinct response patterns in human anterior temporal cortex
TLDR
Responses elicited by the face images were distinct in aIT but not in the FFA, suggesting that individual-level face information is likely to be present in both regions, but the data suggest that it is more pronounced in a IT.
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
TLDR
The results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.
Representational dynamics of object vision: the first 1000 ms.
TLDR
The stationarity of patterns of activity in the brain that encode object category information and show these patterns vary over time are examined, suggesting the brain might use flexible time varying codes to represent visual object categories.
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