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
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| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1987 |
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| Umfang: |
7 |
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| Reproduktion: |
Springer Online Journal Archives 1860-2002 |
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| Übergeordnetes Werk: |
in: Medical & biological engineering & computing - 2000, 25(1987) vom: Apr., Seite 366-372 |
| Übergeordnetes Werk: |
volume:25 ; year:1987 ; month:04 ; pages:366-372 ; extent:7 |
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NLEJ206728794 |
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| 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. | ||
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(DE-627)NLEJ206728794 DE-627 ger DE-627 rakwb eng Intelligent software for spike separation in multiunit recordings 1987 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Springer Online Journal Archives 1860-2002 Vibert, J. -F. oth Albert, J. -N. oth Costa, J. oth in Medical & biological engineering & computing 2000 25(1987) vom: Apr., Seite 366-372 (DE-627)NLEJ188988025 (DE-600)2052667-2 1741-0444 nnns volume:25 year:1987 month:04 pages:366-372 extent:7 http://dx.doi.org/10.1007/BF02443355 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 25 1987 4 366-372 7 |
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(DE-627)NLEJ206728794 DE-627 ger DE-627 rakwb eng Intelligent software for spike separation in multiunit recordings 1987 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Springer Online Journal Archives 1860-2002 Vibert, J. -F. oth Albert, J. -N. oth Costa, J. oth in Medical & biological engineering & computing 2000 25(1987) vom: Apr., Seite 366-372 (DE-627)NLEJ188988025 (DE-600)2052667-2 1741-0444 nnns volume:25 year:1987 month:04 pages:366-372 extent:7 http://dx.doi.org/10.1007/BF02443355 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 25 1987 4 366-372 7 |
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(DE-627)NLEJ206728794 DE-627 ger DE-627 rakwb eng Intelligent software for spike separation in multiunit recordings 1987 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Springer Online Journal Archives 1860-2002 Vibert, J. -F. oth Albert, J. -N. oth Costa, J. oth in Medical & biological engineering & computing 2000 25(1987) vom: Apr., Seite 366-372 (DE-627)NLEJ188988025 (DE-600)2052667-2 1741-0444 nnns volume:25 year:1987 month:04 pages:366-372 extent:7 http://dx.doi.org/10.1007/BF02443355 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 25 1987 4 366-372 7 |
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(DE-627)NLEJ206728794 DE-627 ger DE-627 rakwb eng Intelligent software for spike separation in multiunit recordings 1987 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Springer Online Journal Archives 1860-2002 Vibert, J. -F. oth Albert, J. -N. oth Costa, J. oth in Medical & biological engineering & computing 2000 25(1987) vom: Apr., Seite 366-372 (DE-627)NLEJ188988025 (DE-600)2052667-2 1741-0444 nnns volume:25 year:1987 month:04 pages:366-372 extent:7 http://dx.doi.org/10.1007/BF02443355 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 25 1987 4 366-372 7 |
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(DE-627)NLEJ206728794 DE-627 ger DE-627 rakwb eng Intelligent software for spike separation in multiunit recordings 1987 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Springer Online Journal Archives 1860-2002 Vibert, J. -F. oth Albert, J. -N. oth Costa, J. oth in Medical & biological engineering & computing 2000 25(1987) vom: Apr., Seite 366-372 (DE-627)NLEJ188988025 (DE-600)2052667-2 1741-0444 nnns volume:25 year:1987 month:04 pages:366-372 extent:7 http://dx.doi.org/10.1007/BF02443355 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 25 1987 4 366-372 7 |
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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. |
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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. |
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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. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ206728794</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505192859.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">070528s1987 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ206728794</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="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="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</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="533" ind1=" " ind2=" "><subfield code="f">Springer Online Journal Archives 1860-2002</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vibert, J. -F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Albert, J. -N.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Costa, J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">in</subfield><subfield code="t">Medical & biological engineering & computing</subfield><subfield code="d">2000</subfield><subfield code="g">25(1987) vom: Apr., Seite 366-372</subfield><subfield code="w">(DE-627)NLEJ188988025</subfield><subfield code="w">(DE-600)2052667-2</subfield><subfield code="x">1741-0444</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">month:04</subfield><subfield code="g">pages:366-372</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1007/BF02443355</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-SOJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</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="c">4</subfield><subfield code="h">366-372</subfield><subfield code="g">7</subfield></datafield></record></collection>
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