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

<div> <h2>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'</h2> <p>&nbsp;</p> </div> <div> <h3>Summary</h3> </div> <p>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.</p> <p>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.</p> <p>The monkey dataset has already been published <a href="https://figshare.com/collections/Monkey_V1_and_V4_single-cell_responses_to_natural_images_ephys_Data_from_Cadena_et_al_2024_/6658331/2">here</a>.</p> <div> <h3>Repository structure</h3> </div> <p>The datasets are divided into different experimental paradigms.</p> <p><strong>Imagenet scans</strong> (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 <em>in-silico</em> 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.</p> <p><strong>Dotmap and orientation scans </strong>("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.</p> <div> <h3>Related Repositories</h3> </div> <p>We used the following Github repositories for analysis, which are all publicly available:</p> <ul> <li>Processing of the calcium data: <a href="https://github.com/cajal/pipeline">https://github.com/cajal/pipeline</a></li> <li>Model training of mouse datasets: <a href="https://github.com/sinzlab/nnidentify">https://github.com/sinzlab/nnidentify</a></li> <li>MEI optimization: <a href="https://github.com/sinzlab/mei/tree/inception_loop">https://github.com/sinzlab/mei/tree/inception_loop</a></li> <li>Gabor optimization: <a href="https://github.com/mohammadbashiri/fitgabor">https://github.com/mohammadbashiri/fitgabor</a></li> </ul>