2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1A.62
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Measuring the Spatial Scale of Brain Representations
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1174-0
Authors: Avital Hahamy, University College London, United Kingdom; Tim Behrens, University of Oxford, United Kingdom
Abstract: Understanding how the brain encodes information is one of the core questions in cognitive neuroscience. This question has been tackled by measuring fine-grained fMRI activity patterns across voxels, termed brain representations. These measured representations likely capture gross variations in activity across functional sub-regions, which are reflected in patterns of low spatial frequency. However, it is unclear whether patterns that are not driven by functional/anatomical structure (and are therefore expected to contain higher spatial frequencies) also contribute to these representations. Such rugged patterns have the potential to reflect more intricate stimulus-related information. Here we present a novel method for separating the high- from the low-frequency patterns, and evaluating whether these patterns contain reliable information. By relying on cross-subject temporal synchronization of brain activity and within-subject consistency of activity patterns, our method provides evidence that, at least in sensory brain regions, high-frequency patterns hold reliable information. Using the same method we also demonstrate that many of these activity patterns are unique to each individual. These results demonstrate the potential of our novel method to shed new light on the types of information conveyed by brain representation.