Temporal-Pattern Learning in Neural Models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1985
|
| Schriftenreihe: | Lecture Notes in Biomathematics, Brain Theory Subseries
63 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experimental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specification of the pacemaker neuron model proposed together with its validation through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying several factors related to the connectivity, the intraneuronal parameters, the initial state and the stimulation conditions (Chapter 5). This work was initiated at the Computer and Information Science Department of the University of Massachusetts, Amherst, and completed at the Institut de cibernetica of the Universitat Politecnica de Catalunya, Barcelona. Computers at the latter place have adopted Catalan as their mother tongue and thus some computer-made figures in this monograph, specially those in Chapter 5, appear labeled in that tongue |
| Beschreibung: | 1 Online-Ressource (VII, 227 p) |
| ISBN: | 9783642515804 9783540160465 |
| ISSN: | 0341-633X |
| DOI: | 10.1007/978-3-642-51580-4 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV042422577 | ||
| 003 | DE-604 | ||
| 005 | 20170925 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s1985 |||| o||u| ||||||eng d | ||
| 020 | |a 9783642515804 |c Online |9 978-3-642-51580-4 | ||
| 020 | |a 9783540160465 |c Print |9 978-3-540-16046-5 | ||
| 024 | 7 | |a 10.1007/978-3-642-51580-4 |2 doi | |
| 035 | |a (OCoLC)1184483026 | ||
| 035 | |a (DE-599)BVBBV042422577 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-703 |a DE-91 |a DE-634 | ||
| 082 | 0 | |a 519 |2 23 | |
| 084 | |a MAT 000 |2 stub | ||
| 100 | 1 | |a Genís, Carme Torras |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Temporal-Pattern Learning in Neural Models |c by Carme Torras Genís |
| 264 | 1 | |a Berlin, Heidelberg |b Springer Berlin Heidelberg |c 1985 | |
| 300 | |a 1 Online-Ressource (VII, 227 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 1 | |a Lecture Notes in Biomathematics, Brain Theory Subseries |v 63 |x 0341-633X | |
| 500 | |a While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experimental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specification of the pacemaker neuron model proposed together with its validation through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying several factors related to the connectivity, the intraneuronal parameters, the initial state and the stimulation conditions (Chapter 5). This work was initiated at the Computer and Information Science Department of the University of Massachusetts, Amherst, and completed at the Institut de cibernetica of the Universitat Politecnica de Catalunya, Barcelona. Computers at the latter place have adopted Catalan as their mother tongue and thus some computer-made figures in this monograph, specially those in Chapter 5, appear labeled in that tongue | ||
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Algebra | |
| 650 | 4 | |a Applications of Mathematics | |
| 650 | 4 | |a Mathematical and Computational Biology | |
| 650 | 4 | |a Mathematik | |
| 650 | 0 | 7 | |a Mathematisches Modell |0 (DE-588)4114528-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neurophysiologie |0 (DE-588)4041897-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Biomathematik |0 (DE-588)4139408-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Modellierung |0 (DE-588)4170297-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Zeitverhalten |0 (DE-588)4238464-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Neurophysiologie |0 (DE-588)4041897-2 |D s |
| 689 | 0 | 1 | |a Biomathematik |0 (DE-588)4139408-2 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Neurophysiologie |0 (DE-588)4041897-2 |D s |
| 689 | 1 | 1 | |a Mathematisches Modell |0 (DE-588)4114528-8 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 689 | 2 | 0 | |a Modellierung |0 (DE-588)4170297-9 |D s |
| 689 | 2 | |8 3\p |5 DE-604 | |
| 689 | 3 | 0 | |a Zeitverhalten |0 (DE-588)4238464-3 |D s |
| 689 | 3 | |8 4\p |5 DE-604 | |
| 830 | 0 | |a Lecture Notes in Biomathematics, Brain Theory Subseries |v 63 |w (DE-604)BV005875746 |9 63 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-642-51580-4 |x Verlag |3 Volltext |
| 912 | |a ZDB-2-SMA |a ZDB-2-BAE | ||
| 940 | 1 | |q ZDB-2-SMA_Archive | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027857994 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 3\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 4\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804153097199550464 |
|---|---|
| any_adam_object | |
| author | Genís, Carme Torras |
| author_facet | Genís, Carme Torras |
| author_role | aut |
| author_sort | Genís, Carme Torras |
| author_variant | c t g ct ctg |
| building | Verbundindex |
| bvnumber | BV042422577 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)1184483026 (DE-599)BVBBV042422577 |
| dewey-full | 519 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519 |
| dewey-search | 519 |
| dewey-sort | 3519 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-3-642-51580-4 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04144nmm a2200661zcb4500</leader><controlfield tag="001">BV042422577</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20170925 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1985 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783642515804</subfield><subfield code="c">Online</subfield><subfield code="9">978-3-642-51580-4</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783540160465</subfield><subfield code="c">Print</subfield><subfield code="9">978-3-540-16046-5</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-3-642-51580-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1184483026</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042422577</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Genís, Carme Torras</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Temporal-Pattern Learning in Neural Models</subfield><subfield code="c">by Carme Torras Genís</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin, Heidelberg</subfield><subfield code="b">Springer Berlin Heidelberg</subfield><subfield code="c">1985</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (VII, 227 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Lecture Notes in Biomathematics, Brain Theory Subseries</subfield><subfield code="v">63</subfield><subfield code="x">0341-633X</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experimental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specification of the pacemaker neuron model proposed together with its validation through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying several factors related to the connectivity, the intraneuronal parameters, the initial state and the stimulation conditions (Chapter 5). This work was initiated at the Computer and Information Science Department of the University of Massachusetts, Amherst, and completed at the Institut de cibernetica of the Universitat Politecnica de Catalunya, Barcelona. Computers at the latter place have adopted Catalan as their mother tongue and thus some computer-made figures in this monograph, specially those in Chapter 5, appear labeled in that tongue</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algebra</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Applications of Mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical and Computational Biology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neurophysiologie</subfield><subfield code="0">(DE-588)4041897-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Biomathematik</subfield><subfield code="0">(DE-588)4139408-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Modellierung</subfield><subfield code="0">(DE-588)4170297-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Zeitverhalten</subfield><subfield code="0">(DE-588)4238464-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Neurophysiologie</subfield><subfield code="0">(DE-588)4041897-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Biomathematik</subfield><subfield code="0">(DE-588)4139408-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Neurophysiologie</subfield><subfield code="0">(DE-588)4041897-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Modellierung</subfield><subfield code="0">(DE-588)4170297-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="8">3\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="3" ind2="0"><subfield code="a">Zeitverhalten</subfield><subfield code="0">(DE-588)4238464-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="3" ind2=" "><subfield code="8">4\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Lecture Notes in Biomathematics, Brain Theory Subseries</subfield><subfield code="v">63</subfield><subfield code="w">(DE-604)BV005875746</subfield><subfield code="9">63</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-3-642-51580-4</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SMA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SMA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027857994</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">3\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">4\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV042422577 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:11Z |
| institution | BVB |
| isbn | 9783642515804 9783540160465 |
| issn | 0341-633X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027857994 |
| oclc_num | 1184483026 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (VII, 227 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1985 |
| publishDateSearch | 1985 |
| publishDateSort | 1985 |
| publisher | Springer Berlin Heidelberg |
| record_format | marc |
| series | Lecture Notes in Biomathematics, Brain Theory Subseries |
| series2 | Lecture Notes in Biomathematics, Brain Theory Subseries |
| spelling | Genís, Carme Torras Verfasser aut Temporal-Pattern Learning in Neural Models by Carme Torras Genís Berlin, Heidelberg Springer Berlin Heidelberg 1985 1 Online-Ressource (VII, 227 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Biomathematics, Brain Theory Subseries 63 0341-633X While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experimental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specification of the pacemaker neuron model proposed together with its validation through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying several factors related to the connectivity, the intraneuronal parameters, the initial state and the stimulation conditions (Chapter 5). This work was initiated at the Computer and Information Science Department of the University of Massachusetts, Amherst, and completed at the Institut de cibernetica of the Universitat Politecnica de Catalunya, Barcelona. Computers at the latter place have adopted Catalan as their mother tongue and thus some computer-made figures in this monograph, specially those in Chapter 5, appear labeled in that tongue Mathematics Algebra Applications of Mathematics Mathematical and Computational Biology Mathematik Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Neurophysiologie (DE-588)4041897-2 gnd rswk-swf Biomathematik (DE-588)4139408-2 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Zeitverhalten (DE-588)4238464-3 gnd rswk-swf Neurophysiologie (DE-588)4041897-2 s Biomathematik (DE-588)4139408-2 s 1\p DE-604 Mathematisches Modell (DE-588)4114528-8 s 2\p DE-604 Modellierung (DE-588)4170297-9 s 3\p DE-604 Zeitverhalten (DE-588)4238464-3 s 4\p DE-604 Lecture Notes in Biomathematics, Brain Theory Subseries 63 (DE-604)BV005875746 63 https://doi.org/10.1007/978-3-642-51580-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Genís, Carme Torras Temporal-Pattern Learning in Neural Models Lecture Notes in Biomathematics, Brain Theory Subseries Mathematics Algebra Applications of Mathematics Mathematical and Computational Biology Mathematik Mathematisches Modell (DE-588)4114528-8 gnd Neurophysiologie (DE-588)4041897-2 gnd Biomathematik (DE-588)4139408-2 gnd Modellierung (DE-588)4170297-9 gnd Zeitverhalten (DE-588)4238464-3 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4041897-2 (DE-588)4139408-2 (DE-588)4170297-9 (DE-588)4238464-3 |
| title | Temporal-Pattern Learning in Neural Models |
| title_auth | Temporal-Pattern Learning in Neural Models |
| title_exact_search | Temporal-Pattern Learning in Neural Models |
| title_full | Temporal-Pattern Learning in Neural Models by Carme Torras Genís |
| title_fullStr | Temporal-Pattern Learning in Neural Models by Carme Torras Genís |
| title_full_unstemmed | Temporal-Pattern Learning in Neural Models by Carme Torras Genís |
| title_short | Temporal-Pattern Learning in Neural Models |
| title_sort | temporal pattern learning in neural models |
| topic | Mathematics Algebra Applications of Mathematics Mathematical and Computational Biology Mathematik Mathematisches Modell (DE-588)4114528-8 gnd Neurophysiologie (DE-588)4041897-2 gnd Biomathematik (DE-588)4139408-2 gnd Modellierung (DE-588)4170297-9 gnd Zeitverhalten (DE-588)4238464-3 gnd |
| topic_facet | Mathematics Algebra Applications of Mathematics Mathematical and Computational Biology Mathematik Mathematisches Modell Neurophysiologie Biomathematik Modellierung Zeitverhalten |
| url | https://doi.org/10.1007/978-3-642-51580-4 |
| volume_link | (DE-604)BV005875746 |
| work_keys_str_mv | AT geniscarmetorras temporalpatternlearninginneuralmodels |