Dataset Open Access

Ground truth recordings for validation of spike sorting algorithms

Giulia LB Spampinato; Elric Esposito; Pierre Yger; Jens Duebel; Serge Picaud; Olivier Marre


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{
  "description": "<p><strong>Ground-truth recordings for validation of spike sorting algorithms</strong><br>\n&nbsp;</p>\n\n<p>This datasets is composed of simultaneous loose patch recordings of Ganglion Cells in mice retina, combined with dense extra-cellular recordings (252 channels). The details of the dataset can be found here <a href=\"https://elifesciences.org/articles/34518\">https://elifesciences.org/articles/34518</a></p>\n\n<p><strong>Probe layout</strong></p>\n\n<p>The probe layout can be found as mea_256.prb. This is a 16x16 Multi Electrode Array with 30um spacing. Only 252 channels are extra-cellular signals, and the 4 corners are devoted to triggers/sync/juxta.</p>\n\n<p><strong>Struture of the data</strong></p>\n\n<p>In this dataset, you will find several individual recordings, at max 5min long each (but please do not hesitate to contact us if interested by longer recordings).&nbsp;The extra-cellular data are saved as 16bits unsigned integer, with a variable offset at the beginning of the file. The value of this offset is given, for every datafile, in the additional text file (padding value (see following for more details)).&nbsp;The files have already been filtered with a Butterworth filter of order 3 with a cut-off frequency at 100Hz</p>\n\n<p><strong>Structure of a given dataset</strong></p>\n\n<p>Please read carefully the following to understand how to load and perform spike sorting with the data. In every .tar.gz file, you will find:</p>\n\n<ul>\n\t<li>&nbsp;a jpg image, displaying a small chunk of the juxta-cellular signal (top left), with detected peaks and threshold. The extra-cellular spike triggered waveform, across all channels, for the juxta-spike times (top right). In the bottom, you can see the juxta-cellular spikes, for all the detected triggers (left), and on the right the voltage on the channel where the Spike Triggered Average of the extra-cellular waveform is peaking the most.</li>\n\t<li>a file .juxta.raw, as float32, with the juxta-cellular trace at 20kHz, no data offset</li>\n\t<li>a file .raw, as uint16, with the extra-cellular signals recorded for 256 channels at a sampling rate of 20kHZ. In fact, only 252 channels are extra-cellular signals, the 4 corners of the arrays are devoted to juxta-cellular and sync signals (see probe layout mea_256.prb)</li>\n\t<li>a file .triggers.npy containing the spike times of the juxta-cellular spikes, detected using a threshold of k.MAD. The exact value of k can vary on a per dataset basis, and is written in the .txt file (threshold)</li>\n\t<li>a .txt file describing some information for a given dataset, such as the threshold value used to detect the spikes, the channel in the raw file where the juxta-cellular signal is located, the minimal value of the peak for the STA (and on which channel it is located), and the header size to read the raw data</li>\n\t<li>a .params file, if you want to analyze the data with SpyKING CIRCUS</li>\n</ul>\n\n<p><strong>How to load the raw data in numpy</strong></p>\n\n<pre><code class=\"language-python\">#Using the offset value from the txt file, we can load the data with memmap arrays\n\ndata=numpy.memmap('mydata.raw', dtype='uint16', offset=offset, mode='r')\ndata=data.reshape(len(data)//256, 256)\n\n#Then for example, to display the first second of channel 0\none_channel = data[:20000, 0].astype('float32')\n\n#If we want to center data around 0\none_channel -= 2**15 - 1\n\n#And if we want to display data in micro volt, we must use the gain factor of 0.1042 provided in the header\none_channel *= 0.1042</code></pre>\n\n<p>&nbsp;</p>", 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@type": "Person", 
      "name": "Giulia LB Spampinato"
    }, 
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@type": "Person", 
      "name": "Elric Esposito"
    }, 
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@id": "https://orcid.org/0000-0003-1376-5240", 
      "@type": "Person", 
      "name": "Pierre Yger"
    }, 
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@type": "Person", 
      "name": "Jens Duebel"
    }, 
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@type": "Person", 
      "name": "Serge Picaud"
    }, 
    {
      "affiliation": "Institut de la Vision - INSERM URMS 968, France", 
      "@id": "https://orcid.org/0000-0002-0090-6190", 
      "@type": "Person", 
      "name": "Olivier Marre"
    }
  ], 
  "url": "https://zenodo.org/record/1205233", 
  "datePublished": "2018-03-22", 
  "version": "1.0", 
  "keywords": [
    "neuroscience, spike sorting, ground-truth, spyking circus"
  ], 
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  "name": "Ground truth recordings for validation of spike sorting algorithms"
}
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