Data-driven computational neuroscience: machine learning and statistical models
Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscien...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2020
|
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 FUBA1 URL des Erstveröffentlichers |
| Zusammenfassung: | Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 12 Nov 2020) |
| Beschreibung: | 1 Online-Ressource (xviii, 689 Seiten) |
| ISBN: | 9781108642989 |
| DOI: | 10.1017/9781108642989 |
Internformat
MARC
| LEADER | 00000nmm a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047052238 | ||
| 003 | DE-604 | ||
| 005 | 20231205 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 201208s2020 |||| o||u| ||||||eng d | ||
| 020 | |a 9781108642989 |c Online |9 978-1-108-64298-9 | ||
| 024 | 7 | |a 10.1017/9781108642989 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9781108642989 | ||
| 035 | |a (OCoLC)1226700289 | ||
| 035 | |a (DE-599)BVBBV047052238 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-188 |a DE-12 |a DE-92 |a DE-11 | ||
| 082 | 0 | |a 612.8 | |
| 084 | |a CM 4000 |0 (DE-625)18951: |2 rvk | ||
| 084 | |a CM 4400 |0 (DE-625)18955: |2 rvk | ||
| 084 | |a CZ 1310 |0 (DE-625)19230: |2 rvk | ||
| 084 | |a WC 7000 |0 (DE-625)148142: |2 rvk | ||
| 084 | |a WC 7600 |0 (DE-625)148143: |2 rvk | ||
| 100 | 1 | |a Bielza, Concha |d 1966- |e Verfasser |0 (DE-588)1212808150 |4 aut | |
| 245 | 1 | 0 | |a Data-driven computational neuroscience |b machine learning and statistical models |c Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid) |
| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore |b Cambridge University Press |c 2020 | |
| 300 | |a 1 Online-Ressource (xviii, 689 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Title from publisher's bibliographic system (viewed on 12 Nov 2020) | ||
| 520 | |a Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered | ||
| 650 | 4 | |a Neurosciences / Data processing | |
| 650 | 4 | |a Neurosciences / Statistical methods | |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neurowissenschaften |0 (DE-588)7555119-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | 2 | |a Neurowissenschaften |0 (DE-588)7555119-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Larrañaga, Pedro |d 1958- |e Verfasser |0 (DE-588)1222979357 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 978-1-108-49370-3 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/9781108642989 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO |a ZDB-20-CFS | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032459576 | ||
| 966 | e | |u https://doi.org/10.1017/9781108642989 |l BSB01 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781108642989 |l FHN01 |p ZDB-20-CBO |q FHN_PDA_CBO_Kauf |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781108642989 |l FUBA1 |p ZDB-20-CFS |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804182040874057728 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Bielza, Concha 1966- Larrañaga, Pedro 1958- |
| author_GND | (DE-588)1212808150 (DE-588)1222979357 |
| author_facet | Bielza, Concha 1966- Larrañaga, Pedro 1958- |
| author_role | aut aut |
| author_sort | Bielza, Concha 1966- |
| author_variant | c b cb p l pl |
| building | Verbundindex |
| bvnumber | BV047052238 |
| classification_rvk | CM 4000 CM 4400 CZ 1310 WC 7000 WC 7600 |
| collection | ZDB-20-CBO ZDB-20-CFS |
| ctrlnum | (ZDB-20-CBO)CR9781108642989 (OCoLC)1226700289 (DE-599)BVBBV047052238 |
| dewey-full | 612.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 612 - Human physiology |
| dewey-raw | 612.8 |
| dewey-search | 612.8 |
| dewey-sort | 3612.8 |
| dewey-tens | 610 - Medicine and health |
| discipline | Biologie Psychologie Medizin |
| discipline_str_mv | Biologie Psychologie Medizin |
| doi_str_mv | 10.1017/9781108642989 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03740nmm a2200565 c 4500</leader><controlfield tag="001">BV047052238</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20231205 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">201208s2020 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781108642989</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-108-64298-9</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781108642989</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9781108642989</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1226700289</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047052238</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-188</subfield><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-11</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">612.8</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CM 4000</subfield><subfield code="0">(DE-625)18951:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CM 4400</subfield><subfield code="0">(DE-625)18955:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CZ 1310</subfield><subfield code="0">(DE-625)19230:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WC 7000</subfield><subfield code="0">(DE-625)148142:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WC 7600</subfield><subfield code="0">(DE-625)148143:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bielza, Concha</subfield><subfield code="d">1966-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1212808150</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data-driven computational neuroscience</subfield><subfield code="b">machine learning and statistical models</subfield><subfield code="c">Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xviii, 689 Seiten)</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="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 12 Nov 2020)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neurosciences / Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neurosciences / Statistical methods</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neurowissenschaften</subfield><subfield code="0">(DE-588)7555119-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Neurowissenschaften</subfield><subfield code="0">(DE-588)7555119-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Larrañaga, Pedro</subfield><subfield code="d">1958-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1222979357</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-1-108-49370-3</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/9781108642989</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield><subfield code="a">ZDB-20-CFS</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032459576</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781108642989</subfield><subfield code="l">BSB01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781108642989</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO_Kauf</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781108642989</subfield><subfield code="l">FUBA1</subfield><subfield code="p">ZDB-20-CFS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047052238 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:08:28Z |
| indexdate | 2024-07-10T09:01:14Z |
| institution | BVB |
| isbn | 9781108642989 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032459576 |
| oclc_num | 1226700289 |
| open_access_boolean | |
| owner | DE-188 DE-12 DE-92 DE-11 |
| owner_facet | DE-188 DE-12 DE-92 DE-11 |
| physical | 1 Online-Ressource (xviii, 689 Seiten) |
| psigel | ZDB-20-CBO ZDB-20-CFS ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO_Kauf |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Bielza, Concha 1966- Verfasser (DE-588)1212808150 aut Data-driven computational neuroscience machine learning and statistical models Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid) Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2020 1 Online-Ressource (xviii, 689 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 12 Nov 2020) Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered Neurosciences / Data processing Neurosciences / Statistical methods Statistik (DE-588)4056995-0 gnd rswk-swf Neurowissenschaften (DE-588)7555119-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Statistik (DE-588)4056995-0 s Neurowissenschaften (DE-588)7555119-6 s DE-604 Larrañaga, Pedro 1958- Verfasser (DE-588)1222979357 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-108-49370-3 https://doi.org/10.1017/9781108642989 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Bielza, Concha 1966- Larrañaga, Pedro 1958- Data-driven computational neuroscience machine learning and statistical models Neurosciences / Data processing Neurosciences / Statistical methods Statistik (DE-588)4056995-0 gnd Neurowissenschaften (DE-588)7555119-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)7555119-6 (DE-588)4193754-5 |
| title | Data-driven computational neuroscience machine learning and statistical models |
| title_auth | Data-driven computational neuroscience machine learning and statistical models |
| title_exact_search | Data-driven computational neuroscience machine learning and statistical models |
| title_exact_search_txtP | Data-driven computational neuroscience machine learning and statistical models |
| title_full | Data-driven computational neuroscience machine learning and statistical models Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid) |
| title_fullStr | Data-driven computational neuroscience machine learning and statistical models Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid) |
| title_full_unstemmed | Data-driven computational neuroscience machine learning and statistical models Concha Bielza (Universidad Politécnica de Madrid), Pedro Larrañaga (Universidad Politécnica de Madrid) |
| title_short | Data-driven computational neuroscience |
| title_sort | data driven computational neuroscience machine learning and statistical models |
| title_sub | machine learning and statistical models |
| topic | Neurosciences / Data processing Neurosciences / Statistical methods Statistik (DE-588)4056995-0 gnd Neurowissenschaften (DE-588)7555119-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neurosciences / Data processing Neurosciences / Statistical methods Statistik Neurowissenschaften Maschinelles Lernen |
| url | https://doi.org/10.1017/9781108642989 |
| work_keys_str_mv | AT bielzaconcha datadrivencomputationalneurosciencemachinelearningandstatisticalmodels AT larranagapedro datadrivencomputationalneurosciencemachinelearningandstatisticalmodels |