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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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        <foaf:name>Pierre Yger</foaf:name>
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        <foaf:name>Olivier Marre</foaf:name>
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    <dct:title>Ground truth recordings for validation of spike sorting algorithms</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2018</dct:issued>
    <dcat:keyword>neuroscience, spike sorting, ground-truth, spyking circus</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2018-03-22</dct:issued>
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    <dct:description>&lt;p&gt;&lt;strong&gt;Ground-truth recordings for validation of spike sorting algorithms&lt;/strong&gt;&lt;br&gt; &amp;nbsp;&lt;/p&gt; &lt;p&gt;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 &lt;a href="https://elifesciences.org/articles/34518"&gt;https://elifesciences.org/articles/34518&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Probe layout&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Struture of the data&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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).&amp;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)).&amp;nbsp;The files have already been filtered with a Butterworth filter of order 3 with a cut-off frequency at 100Hz&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Structure of a given dataset&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&amp;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.&lt;/li&gt; &lt;li&gt;a file .juxta.raw, as float32, with the juxta-cellular trace at 20kHz, no data offset&lt;/li&gt; &lt;li&gt;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)&lt;/li&gt; &lt;li&gt;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)&lt;/li&gt; &lt;li&gt;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&lt;/li&gt; &lt;li&gt;a .params file, if you want to analyze the data with SpyKING CIRCUS&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;How to load the raw data in numpy&lt;/strong&gt;&lt;/p&gt; &lt;pre&gt;&lt;code class="language-python"&gt;#Using the offset value from the txt file, we can load the data with memmap arrays data=numpy.memmap('mydata.raw', dtype='uint16', offset=offset, mode='r') data=data.reshape(len(data)//256, 256) #Then for example, to display the first second of channel 0 one_channel = data[:20000, 0].astype('float32') #If we want to center data around 0 one_channel -= 2**15 - 1 #And if we want to display data in micro volt, we must use the gain factor of 0.1042 provided in the header one_channel *= 0.1042&lt;/code&gt;&lt;/pre&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
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