Intelligent software for spike separation in multiunit recordings
Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; othe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vibert, J. -F. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1987 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© IFMBE 1987 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Kluwer Academic Publishers, 1977, 25(1987), 4 vom: Juli, Seite 366-372 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:1987 ; number:4 ; month:07 ; pages:366-372 |
| Links: |
|---|
| DOI / URN: |
10.1007/BF02443355 |
|---|
| Katalog-ID: |
OLC203866093X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC203866093X | ||
| 003 | DE-627 | ||
| 005 | 20230517133210.0 | ||
| 007 | tu | ||
| 008 | 200819s1987 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/BF02443355 |2 doi | |
| 035 | |a (DE-627)OLC203866093X | ||
| 035 | |a (DE-He213)BF02443355-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |a 660 |a 570 |q VZ |
| 084 | |a 12 |2 ssgn | ||
| 100 | 1 | |a Vibert, J. -F. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Intelligent software for spike separation in multiunit recordings |
| 264 | 1 | |c 1987 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © IFMBE 1987 | ||
| 520 | |a Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. | ||
| 650 | 4 | |a Algorithm | |
| 650 | 4 | |a Multiunit recordings | |
| 650 | 4 | |a Spike separation | |
| 650 | 4 | |a Spike trains | |
| 700 | 1 | |a Albert, J. -N. |4 aut | |
| 700 | 1 | |a Costa, J. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical & biological engineering & computing |d Kluwer Academic Publishers, 1977 |g 25(1987), 4 vom: Juli, Seite 366-372 |w (DE-627)129858552 |w (DE-600)282327-5 |w (DE-576)015165507 |x 0140-0118 |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:1987 |g number:4 |g month:07 |g pages:366-372 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/BF02443355 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-CHE | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OLC-DE-84 | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_34 | ||
| 912 | |a GBV_ILN_55 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4028 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4219 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4317 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 1987 |e 4 |c 07 |h 366-372 | ||
| author_variant |
j - v j- j-v j - a j- j-a j c jc |
|---|---|
| matchkey_str |
article:01400118:1987----::nelgnsfwrfrpksprtoiml |
| hierarchy_sort_str |
1987 |
| publishDate |
1987 |
| allfields |
10.1007/BF02443355 doi (DE-627)OLC203866093X (DE-He213)BF02443355-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Vibert, J. -F. verfasserin aut Intelligent software for spike separation in multiunit recordings 1987 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 1987 Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. Algorithm Multiunit recordings Spike separation Spike trains Albert, J. -N. aut Costa, J. aut Enthalten in Medical & biological engineering & computing Kluwer Academic Publishers, 1977 25(1987), 4 vom: Juli, Seite 366-372 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:25 year:1987 number:4 month:07 pages:366-372 https://doi.org/10.1007/BF02443355 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 AR 25 1987 4 07 366-372 |
| spelling |
10.1007/BF02443355 doi (DE-627)OLC203866093X (DE-He213)BF02443355-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Vibert, J. -F. verfasserin aut Intelligent software for spike separation in multiunit recordings 1987 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 1987 Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. Algorithm Multiunit recordings Spike separation Spike trains Albert, J. -N. aut Costa, J. aut Enthalten in Medical & biological engineering & computing Kluwer Academic Publishers, 1977 25(1987), 4 vom: Juli, Seite 366-372 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:25 year:1987 number:4 month:07 pages:366-372 https://doi.org/10.1007/BF02443355 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 AR 25 1987 4 07 366-372 |
| allfields_unstemmed |
10.1007/BF02443355 doi (DE-627)OLC203866093X (DE-He213)BF02443355-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Vibert, J. -F. verfasserin aut Intelligent software for spike separation in multiunit recordings 1987 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 1987 Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. Algorithm Multiunit recordings Spike separation Spike trains Albert, J. -N. aut Costa, J. aut Enthalten in Medical & biological engineering & computing Kluwer Academic Publishers, 1977 25(1987), 4 vom: Juli, Seite 366-372 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:25 year:1987 number:4 month:07 pages:366-372 https://doi.org/10.1007/BF02443355 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 AR 25 1987 4 07 366-372 |
| allfieldsGer |
10.1007/BF02443355 doi (DE-627)OLC203866093X (DE-He213)BF02443355-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Vibert, J. -F. verfasserin aut Intelligent software for spike separation in multiunit recordings 1987 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 1987 Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. Algorithm Multiunit recordings Spike separation Spike trains Albert, J. -N. aut Costa, J. aut Enthalten in Medical & biological engineering & computing Kluwer Academic Publishers, 1977 25(1987), 4 vom: Juli, Seite 366-372 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:25 year:1987 number:4 month:07 pages:366-372 https://doi.org/10.1007/BF02443355 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 AR 25 1987 4 07 366-372 |
| allfieldsSound |
10.1007/BF02443355 doi (DE-627)OLC203866093X (DE-He213)BF02443355-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Vibert, J. -F. verfasserin aut Intelligent software for spike separation in multiunit recordings 1987 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 1987 Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. Algorithm Multiunit recordings Spike separation Spike trains Albert, J. -N. aut Costa, J. aut Enthalten in Medical & biological engineering & computing Kluwer Academic Publishers, 1977 25(1987), 4 vom: Juli, Seite 366-372 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:25 year:1987 number:4 month:07 pages:366-372 https://doi.org/10.1007/BF02443355 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 AR 25 1987 4 07 366-372 |
| language |
English |
| source |
Enthalten in Medical & biological engineering & computing 25(1987), 4 vom: Juli, Seite 366-372 volume:25 year:1987 number:4 month:07 pages:366-372 |
| sourceStr |
Enthalten in Medical & biological engineering & computing 25(1987), 4 vom: Juli, Seite 366-372 volume:25 year:1987 number:4 month:07 pages:366-372 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Algorithm Multiunit recordings Spike separation Spike trains |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Medical & biological engineering & computing |
| authorswithroles_txt_mv |
Vibert, J. -F. @@aut@@ Albert, J. -N. @@aut@@ Costa, J. @@aut@@ |
| publishDateDaySort_date |
1987-07-01T00:00:00Z |
| hierarchy_top_id |
129858552 |
| dewey-sort |
3610 |
| id |
OLC203866093X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC203866093X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230517133210.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s1987 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/BF02443355</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC203866093X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)BF02443355-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">660</subfield><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vibert, J. -F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Intelligent software for spike separation in multiunit recordings</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1987</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© IFMBE 1987</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiunit recordings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike separation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike trains</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Albert, J. -N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Costa, J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Medical & biological engineering & computing</subfield><subfield code="d">Kluwer Academic Publishers, 1977</subfield><subfield code="g">25(1987), 4 vom: Juli, Seite 366-372</subfield><subfield code="w">(DE-627)129858552</subfield><subfield code="w">(DE-600)282327-5</subfield><subfield code="w">(DE-576)015165507</subfield><subfield code="x">0140-0118</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:1987</subfield><subfield code="g">number:4</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:366-372</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/BF02443355</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_34</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_55</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4028</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4317</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">1987</subfield><subfield code="e">4</subfield><subfield code="c">07</subfield><subfield code="h">366-372</subfield></datafield></record></collection>
|
| author |
Vibert, J. -F. |
| spellingShingle |
Vibert, J. -F. ddc 610 ssgn 12 misc Algorithm misc Multiunit recordings misc Spike separation misc Spike trains Intelligent software for spike separation in multiunit recordings |
| authorStr |
Vibert, J. -F. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129858552 |
| format |
Article |
| dewey-ones |
610 - Medicine & health 660 - Chemical engineering 570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0140-0118 |
| topic_title |
610 660 570 VZ 12 ssgn Intelligent software for spike separation in multiunit recordings Algorithm Multiunit recordings Spike separation Spike trains |
| topic |
ddc 610 ssgn 12 misc Algorithm misc Multiunit recordings misc Spike separation misc Spike trains |
| topic_unstemmed |
ddc 610 ssgn 12 misc Algorithm misc Multiunit recordings misc Spike separation misc Spike trains |
| topic_browse |
ddc 610 ssgn 12 misc Algorithm misc Multiunit recordings misc Spike separation misc Spike trains |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Medical & biological engineering & computing |
| hierarchy_parent_id |
129858552 |
| dewey-tens |
610 - Medicine & health 660 - Chemical engineering 570 - Life sciences; biology |
| hierarchy_top_title |
Medical & biological engineering & computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 |
| title |
Intelligent software for spike separation in multiunit recordings |
| ctrlnum |
(DE-627)OLC203866093X (DE-He213)BF02443355-p |
| title_full |
Intelligent software for spike separation in multiunit recordings |
| author_sort |
Vibert, J. -F. |
| journal |
Medical & biological engineering & computing |
| journalStr |
Medical & biological engineering & computing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
1987 |
| contenttype_str_mv |
txt |
| container_start_page |
366 |
| author_browse |
Vibert, J. -F. Albert, J. -N. Costa, J. |
| container_volume |
25 |
| class |
610 660 570 VZ 12 ssgn |
| format_se |
Aufsätze |
| author-letter |
Vibert, J. -F. |
| doi_str_mv |
10.1007/BF02443355 |
| dewey-full |
610 660 570 |
| title_sort |
intelligent software for spike separation in multiunit recordings |
| title_auth |
Intelligent software for spike separation in multiunit recordings |
| abstract |
Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. © IFMBE 1987 |
| abstractGer |
Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. © IFMBE 1987 |
| abstract_unstemmed |
Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis. © IFMBE 1987 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_11 GBV_ILN_32 GBV_ILN_34 GBV_ILN_55 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4306 GBV_ILN_4317 GBV_ILN_4700 |
| container_issue |
4 |
| title_short |
Intelligent software for spike separation in multiunit recordings |
| url |
https://doi.org/10.1007/BF02443355 |
| remote_bool |
false |
| author2 |
Albert, J. -N. Costa, J. |
| author2Str |
Albert, J. -N. Costa, J. |
| ppnlink |
129858552 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/BF02443355 |
| up_date |
2024-07-03T19:44:12.661Z |
| _version_ |
1803588314239860736 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC203866093X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230517133210.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s1987 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/BF02443355</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC203866093X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)BF02443355-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">660</subfield><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vibert, J. -F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Intelligent software for spike separation in multiunit recordings</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1987</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© IFMBE 1987</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiunit recordings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike separation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike trains</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Albert, J. -N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Costa, J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Medical & biological engineering & computing</subfield><subfield code="d">Kluwer Academic Publishers, 1977</subfield><subfield code="g">25(1987), 4 vom: Juli, Seite 366-372</subfield><subfield code="w">(DE-627)129858552</subfield><subfield code="w">(DE-600)282327-5</subfield><subfield code="w">(DE-576)015165507</subfield><subfield code="x">0140-0118</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:1987</subfield><subfield code="g">number:4</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:366-372</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/BF02443355</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_34</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_55</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4028</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4317</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">1987</subfield><subfield code="e">4</subfield><subfield code="c">07</subfield><subfield code="h">366-372</subfield></datafield></record></collection>
|
| score |
7.4003134 |