Dataset Open Access
Giulia LB Spampinato;
Elric Esposito;
Pierre Yger;
Jens Duebel;
Serge Picaud;
Olivier Marre
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code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2018-03-22</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:zenodo.org:1205233</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="p">Elife</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Institut de la Vision - INSERM URMS 968, France</subfield> <subfield code="a">Giulia LB Spampinato</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Ground truth recordings for validation of spike sorting algorithms</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">http://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p><strong>Ground-truth recordings for validation of spike sorting algorithms</strong><br> &nbsp;</p> <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> <p><strong>Probe layout</strong></p> <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> <p><strong>Struture of the data</strong></p> <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> <p><strong>Structure of a given dataset</strong></p> <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> <ul> <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> <li>a file .juxta.raw, as float32, with the juxta-cellular trace at 20kHz, no data offset</li> <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> <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> <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> <li>a .params file, if you want to analyze the data with SpyKING CIRCUS</li> </ul> <p><strong>How to load the raw data in numpy</strong></p> <pre><code class="language-python">#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</code></pre> <p>&nbsp;</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.1205232</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.1205233</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">dataset</subfield> </datafield> </record>
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Unique downloads | 264 | 264 |