Non-canonical receptive field properties and neuromodulation of feature detecting neurons in flies

This repository contains the data and supplemental data for the following publication:
Städele C., Keleş M.F., Mongeau J.M., Frye M.A. (2020), Non-canonical receptive field properties and neuromodulation of feature detecting neurons in flies, Current Biology

DATA INFO:
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Data is organized by the corresponding Figure number.
Due to storage limitations, and for your convenience, we are providing .mat files that contain the extracted change in fluorescence and visual pattern information rather than original two-photon imaging data. Data files can be opened using MATLAB (The MathWorks, Natick, MA, USA). Data is minimally processed, i.e. MATLAB files contain unprocessed and unfiltered GCAMP6F two-photon imaging data. To generate the .mat files, two-photon images were corrected for motion artifacts. Aligned images were exported to MATLAB and ROI selection performed to extract data from the anatomical regions of interest. For every pixel in the ROI mask, the mean and standard deviation were calculated for the full time series. A test value for each pixel was calculated by the product of mean and standard deviation of each pixel. Pixels with test values greater than or equal to twice the mean value of all test values in the ROI mask were used for analysis. For each stimulus condition, at least three trials were averaged. ΔF/F was calculated by dividing the signal by the mean intensity of the first 20 frames (2 s) preceding the stimulus motion onset.

ANALYSIS SCRIPTS:
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For the original analysis, we used a custom MATLAB toolbox that was developed by B. Hardcastle. Toolbox code is available at https://github.com/bjhardcastle/SlidebookObj.

Further details about each data set can be found in the READ_ME_Figure_X.txt files in the parent directory and the Mendeley Data description associated with the folder.

本存储库包含了以下发表的论文所涉及的数据及补充数据：

Städele C., Keleş M.F., Mongeau J.M., Frye M.A. (2020), 非典型感受野属性及特征检测神经元在果蝇中的神经调节，Current Biology

数据信息：

数据按照对应的图号进行组织。鉴于存储限制，为方便用户使用，我们提供包含提取的荧光变化和视觉模式信息的.mat文件，而非原始的双光子成像数据。这些数据文件可以使用MATLAB（The MathWorks，Natick，MA，USA）打开。数据经过最小化处理，即MATLAB文件中包含未经处理和未过滤的GCAMP6F双光子成像数据。为生成.mat文件，对双光子图像进行了运动伪影的校正。将校准后的图像导出到MATLAB中，并执行感兴趣区域（ROI）的选择，以提取感兴趣解剖区域的数据。对于ROI掩模中的每个像素，计算了整个时间序列的平均值和标准差。每个像素的测试值由该像素的平均值和标准差相乘得到。对于ROI掩模中所有测试值的平均值的两倍及以上值的像素用于分析。对于每个刺激条件，至少平均了三次试验。ΔF/F是通过将信号除以刺激运动开始前前20帧（2秒）的平均强度计算得出的。

分析脚本：

原始分析中，我们使用了由B. Hardcastle开发的定制MATLAB工具箱。工具箱代码可在https://github.com/bjhardcastle/SlidebookObj找到。关于每个数据集的更详细信息，可在父目录中的READ_ME_Figure_X.txt文件以及与文件夹相关的Mendeley Data描述中找到。