Chronic recordings from Neuropixels 2.0 probes in mice

This repository contains the raw data from chronic recordings in the visual cortex of three mice. The data were analyzed and published in Steinmetz, Aydin, Lebedeva, Okun, Pachitariu et al. "Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings", Science 2021 (doi.org/10.1126/science.abf4588).The data pertain to Figure 4 of that paper, where neurons are characterized by their responses to a battery of 112 natural images. The fact that neurons retain the same preferences for natural images over tens of days strongly suggests that the neurons themselves are the same across days.The folder "Images" contains the 112 images that were presented to the mice.The data are from 3 mice:AL031 (“Mouse 3”), recorded with a 1-shank Neuropixels 2.0 probeAL032 (“Mouse 1”), recorded with a 4-shank Neuropixels 2.0 probeAL036 (“Mouse 2”), recorded with a 4-shank Neuropixels 2.0 probeAs detailed in the paper, mice were implanted in the left primary visual cortex, and the probe was cemented to the skull. During recording sessions, mice were head-fixed in front of 3 screens (left, center, right). Each stimulus was presented for 1 s, on the right and central screen, while the left screen was gray. In the interval between stimulus presentations (at least 2 s, as indicated in the files) all screens were gray. Recordings were made in SpikeGLX (https://billkarsh.github.io/SpikeGLX/) so once uncompressed, the data will be in .bin format.Each recording is contained in a folder named after the animal and the date of the recording. Each folder contains:A .cbin file and its accompanying .ch file. This is the compressed raw data. To decompress it, use the following package: https://github.com/int-brain-lab/mtscompA meta file, containing information about the recording settings.A file called stimIDs, containing the reference numbers of images that were presented (560 total; 5 presentations, 112 images each). Each stimID corresponds to an image in the "Images" folder.A file called stimTimes, containing the onset times of each stimulus (also 560 total).A folder called ks, containing the output of a spike sorting algorithm (pyKilosort; https://github.com/int-brain-lab/pykilosort) for that recording. This output can be inspected and curated using phy (https://github.com/cortex-lab/phy). To do so you will need to define a correct path to the data file in the params.py file in the ks folder.Example code to load the dataTo explore the data, we provide the Matlab function call_readRawDataChunk. Using this function, you can read and explore compressed data. To use the function, you will need the code in the repository: https://github.com/fangq/zmat (also available as Matlab Add-On https://uk.mathworks.com/matlabcentral/fileexchange/71434-zmat). Specify the file path, the starting point, and the duration of the chunk you want to explore at the top of the file, and happy exploration!

本仓库收录了三只小鼠视觉皮层慢性记录的原始数据。这些数据经过分析并在Steinmetz、Aydin、Lebedeva、Okun、Pachitariu等人撰写的《Neuropixels 2.0：一种用于稳定、长期脑记录的微型高密度探针》，发表于《科学》杂志2021年（doi.org/10.1126/science.abf4588）一文中得到发表。数据涉及该论文中的第4图，其中神经元通过对其展示的112张自然图像的响应进行特征描述。神经元在数十天内对自然图像保持相同的偏好，这一事实强烈表明神经元本身在每日之间是相同的。文件夹“Images”包含了向小鼠展示的112张图像。数据来自3只小鼠：AL031（“小鼠3”），使用1 Shank Neuropixels 2.0探针进行记录；AL032（“小鼠1”），使用4 Shank Neuropixels 2.0探针进行记录；AL036（“小鼠2”），使用4 Shank Neuropixels 2.0探针进行记录。正如论文中详细所述，小鼠被植入左侧初级视觉皮层，探针被固定在颅骨上。在记录过程中，小鼠被固定在三个屏幕（左、中、右）前方。每个刺激呈现1秒，在右侧和中央屏幕上，而左侧屏幕为灰色。在刺激呈现之间的间隔（至少2秒，如文件所示）内，所有屏幕均为灰色。记录使用SpikeGLX（https://billkarsh.github.io/SpikeGLX/）进行，因此一旦解压，数据将以.bin格式存储。每个记录包含在一个以动物名称和记录日期命名的文件夹中。每个文件夹包含：一个.cbin文件及其相应的.ch文件，这是压缩的原始数据。要解压，请使用以下软件包：https://github.com/int-brain-lab/mtscomp；一个包含记录设置信息的元文件；一个名为stimIDs的文件，包含展示的图像的参考编号（总计560个；每次5次展示，每次112张图像）。每个stimID对应于“Images”文件夹中的一个图像；一个名为stimTimes的文件，包含每个刺激的开始时间（总计560个）；一个名为ks的文件夹，包含该记录的spike sorting算法（pyKilosort；https://github.com/int-brain-lab/pykilosort）的输出。可以使用phy（https://github.com/cortex-lab/phy）检查和编辑这些输出。为此，您需要在ks文件夹中的params.py文件中定义正确的数据文件路径。示例代码用于加载数据。为了探索数据，我们提供了Matlab函数call_readRawDataChunk。使用此函数，您可以读取和探索压缩数据。要使用此函数，您需要从以下仓库中获取代码：https://github.com/fangq/zmat（也可作为Matlab插件https://uk.mathworks.com/matlabcentral/fileexchange/71434-zmat获取）。在文件顶部指定文件路径、起始点和您想要探索的块持续时间，然后开始探索！