A Model of Representational Spaces in Human Cortex

J. Swaroop Guntupalli, Michael Hanke, Yaroslav O. Halchenko, Andrew C. Connolly, Peter J. Ramadge, James V. Haxby

Research output: Contribution to journalArticlepeer-review

120 Scopus citations


Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well-known coarse-scale topographies, such as retinotopy and category selectivity, and goes further to account for fine-scale patterns that are multiplexed with coarse-scale topographies and carry finer distinctions.

Original languageEnglish (US)
Pages (from-to)2919-2934
Number of pages16
JournalCerebral Cortex
Issue number6
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience


  • Functional magnetic resonance imaging (fMRI)
  • Hyperalignment
  • Multivariate pattern analysis (MVPA)
  • Neural decoding
  • Representational similarity analysis (RSA)


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