2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-2A.55
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Trial-by-trial voxelwise noise correlations improve population coding of orientation in human V1
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.2019.1282-0
Authors: Ru-Yuan Zhang, University of Minnesota, United States; Xue-Xin Wei, Columbia University, United States; Xiangbin Teng, Max-Planck-Institute of Empirical Aesthetics, Germany; Kendrick Kay, University of Minnesota, United States
Abstract: Prior empirical and theoretical studies in neurophysiology have suggested that noise correlations between neurons could have a great impact on the fidelity of population codes in macaque visual cortex. However, it remains unclear whether such insights generalize to the large-scale brain activity in human sensory cortex. Here, we use functional magnetic resonance imaging (fMRI) to examine the effect of noise correlations on population coding of orientation in human V1. Trialwise responses of each V1 voxel is estimated for four orientations. We estimate the Fisher information carried by voxel responses for orientation in the empirically observed data (i.e., with noise correlations) and in a simulated regime in which voxelwise noise correlations are absent. Results show that the removal of noise correlations dramatically reduces information by one order of magnitude. This suggests that correlated activity could mediate the accuracy of population codes in the human brain, and that voxelwise noise correlations in human V1 are mostly beneficial, unlike the neuronal noise correlations that are often found to be detrimental.