Published December 9, 2024 | Version v1
Dataset Open

Dataset for: The geometry of correlated variability leads to highly suboptimal discriminative sensory coding

Authors/Creators

  • 1. ROR icon University of California, San Francisco
  • 2. ROR icon Allen Institute for Neural Dynamics
  • 3. ROR icon Lawrence Berkeley National Laboratory

Description

New datasets collected for or used in the paper "The geometry of correlated variability leads to highly suboptimal discriminative sensory coding."

200114_fov2_data.mat and roi_positions.mat contain the per-trial RGC activity used in the analysis and regions of interest coordinates, respectively.

r32_b7.mat and grdid.mat contain the per-trial ECoG activity used in the analysis and electore coordinates, respectively.

Files

Files (1.9 GB)

Name Size Download all
md5:1ae6cdfa3cb1b5acdcb6ec76a130e4d0
168.5 MB Download
md5:3aefc6f010d796ba7d948f50be666bba
4.0 kB Download
md5:49b1359a79c4bc13ffe80d4b66a8a789
1.8 GB Download
md5:cbe56de4761161a8d3e41ad8ae7ff1ae
8.9 kB Download

Additional details

Related works

Is published in
Preprint: 10.1101/2022.03.08.483488 (DOI)

Software

Repository URL
https://github.com/BouchardLab/noise_correlations
Programming language
Python