Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates

Data for the Fu et al. (2024) article: 'Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates' Summary Here we provide the complete data for the article Fu et al., 2024 'Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates': include link. The mouse datasets consists of X individual datasets (i.e. recording scans) of calcium activity of L2/3 and L4 neurons in mouse V1. All datasets were acquired using two-photon imaging of awake, head-fixed mice. The monkey dataset has already been published here. Repository structure The datasets are divided into different experimental paradigms. Imagenet scans (starting with "static*.zip": contain the neuronal activity in response to grayscale naturalistic images. We used these scans for training deep convolutional neural networks to learn an in-silico model of the recorded neuronal population and to optimize MEIs as well as optimal Gabors. The file "ImageNet_Data_Structure.md" contains detailed information about the content of the files. Dotmap and orientation scans ("dataset_*.pkl"): These scans include two types of stimuli: 1) A sparse noise paradigm for mapping receptive fields of visual neurons. 2) Small patches of drifting gratings to study the orientation tuning selectivity at sub receptive field scale of mouse V1 neurons. The file "RFMapping_Orientation_Data_Structure.md" contains information about the content of the files. Related Repositories We used the following Github repositories for analysis, which are all publicly available: Processing of the calcium data: https://github.com/cajal/pipeline Model training of mouse datasets: https://github.com/sinzlab/nnidentify MEI optimization: https://github.com/sinzlab/mei/tree/inception_loop Gabor optimization: https://github.com/mohammadbashiri/fitgabor