DOI: 10.5061/dryad.rv15dv4hb
title: Data from: Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky

description: Color is an important visual feature that informs behavior, and the
 retinal basis for color vision has been studied across various vertebrate
 species. While many studies have investigated how color information is
 processed in visual brain areas of primate species, we have
 limited understanding of how it is organized beyond the retina in other
 species, including most dichromatic mammals. In this study, we
 systematically characterized how color is represented in the primary
 visual cortex (V1) of mice. Using large-scale neuronal recordings and a
 luminance and color noise stimulus, we found that more than a third of
 neurons in mouse V1 are color-opponent in their receptive field center,
 while the receptive field surround predominantly captures luminance
 contrast. Furthermore, we found that color-opponency is especially
 pronounced in posterior V1 that encodes the sky, matching the statistics
 of natural scenes experienced by mice. Using unsupervised clustering, we
 demonstrate that the asymmetry in color representations across cortex can
 be explained by an uneven distribution of green-On/UV-Off color-opponent
 response types that are represented in the upper visual field. Finally, a
 simple model with natural scene-inspired parametric stimuli shows that
 green-On/UV Off color-opponent response types may enhance the
 detection of "predatory"-like dark UV-objects in noisy
 daylight scenes.The results from this study highlight the
 relevance of color processing in the mouse visual system and contribute to
 our understanding of how color information is organized in the visual
 hierarchy across species.
description: # Data from: Asymmetric distribution of color-opponent response types
 across mouse visual cortex supports superior color vision in the sky
 [https://doi.org/10.5061/dryad.rv15dv4hb](https://doi.org/10.5061/dryad.rv15dv4hb) This repository contains the data used in the paper by Franke et al., eLife 2024 (see [here](https://elifesciences.org/reviewed-preprints/89996)), formatted as pandas DataFrames. The data was exported from a DataJoint database (more information on DataJoint available [here](https://www.datajoint.com/)). Additionally, the repository includes the code that defines all the database tables from which the data was fetched. ## Description of the data and file structure * **Spike-triggered averages (STAs):** The files `data_photopic.csv`, `datamesopiclow.csv`, and `datamesopichigh.csv` are DataFrames exported from pandas as CSV files. They contain the STAs of all neurons that passed the quality threshold outlined in the paper for three different light intensities. Each entry is uniquely defined by its `animalid`, `session`, `scanidx`, and `unitid`. For each unit, the STAs are stored as a numpy array in `kernels_processed`, with 240 points corresponding to 4 x 60 points per stimulus condition, sorted as center-green, center-UV, surround-green, and surround-UV. * **PCA weights:** The file `pca_photopic.csv` is a DataFrame exported using pandas as a CSV file. For the same neurons found in `data_photopic.csv`, it contains the PCA weights of the neurons and the PC-reconstructed STAs. The PCA weights were used for Gaussian Mixture Model (GMM) clustering. * **GMM labels:** The file `labels_photopic.csv` is a DataFrame exported using pandas as a CSV file. For the same neurons found in `data_photopic.csv`, it contains the cluster labels obtained from GMM clustering of the PCA weights, as well as the probability for each of the 17 clusters. The mouse retina data we analyzed has been published previously: * [https://zenodo.org/records/3760607](https://zenodo.org/records/3760607) ## Code/Software Data analysis and organization were performed using DataJoint in Python. The data available in this repository were exported from tables within the schema "mouse_color_cs_flicker". The tables of this schema are defined in the corresponding `.py` file available in this repository. The table definitions, including the `make` functions, provide detailed information about the code used for data analysis.