A spatial code for temporal information is necessary for efficient sensory learning

Here we provide a large dataset of neuronal responses in the mouse auditory system to a range of simple sounds. This data is initially published in the paper Bagur, Lebourg et al The "Data" files are organized as follows : Data_XX.mat : one file with the responses all ROIs/units to all 140 sounds for each area. AC data was uploaded in two files with two halves of the neural data set due to file size limitations Clusters_XX.mat : one file with the responses of the clustered ROIS from calcium imaging data go all 140 sounds AnatInfo_AC/ICE.mat : localisation of each ROI from AC and ICE data Correlation_Decoding.mat : the results of core analysis from the paper, to accelerate plotting using the "BasicCorrelationDecodingAnalysis.mlx" code The "Codes" folder contains matlab code showing how to access the data and illustrating how it is organized as well as code to perform basic population level analysis from Bagur, Lebourg et al, illustrating how to calculate noise-free correlation between popultion vector "Packages" are open source code from github written by other members of the scientific community and saved here as a reference : AutoCell : https://github.com/thomasdeneux/Autocell CortexLab suite : https://github.com/cortex-l Phy : https://github.com/cortex-lab/phy Network from Kell et al : https://github.com/mcdermottLab/kelletal2018本数据集收录了小鼠听觉系统神经元对一系列简单声刺激的响应数据，该数据集最初发表于Bagur、Lebourg等人的学术论文中。 数据文件的组织形式如下： - Data_XX.mat：单个文件存储对应脑区内所有感兴趣区域（ROI, Region of Interest）/神经元单元对全部140种声刺激的响应数据。受限于文件大小，听觉皮层（AC, Auditory Cortex）的数据分为两个文件上传，分别存储该神经数据集的两个子集。 - Clusters_XX.mat：单个文件存储经钙成像（Calcium imaging）数据聚类得到的感兴趣区域对全部140种声刺激的响应数据。 - AnatInfo_AC/ICE.mat：存储听觉皮层与ICE脑区各感兴趣区域的定位信息。 - Correlation_Decoding.mat：收录本论文的核心分析结果，可配合"BasicCorrelationDecodingAnalysis.mlx"代码加速绘图流程。 "Codes"文件夹包含Matlab代码，用于演示数据集的访问方法与组织形式，同时提供了基于Bagur、Lebourg等人研究的基础群体水平分析代码，可用于计算群体向量间的无噪声相关性。 "Packages"文件夹收录了科研社区其他成员在GitHub上开源的代码，此处存档以供参考： - AutoCell：https://github.com/thomasdeneux/Autocell - CortexLab 套件：https://github.com/cortex-l - Phy：https://github.com/cortex-lab/phy - Kell等人的网络模型代码：https://github.com/mcdermottLab/kelletal2018