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 neur...
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY
Cambridge University Press
2020
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| Subjects: | |
| Summary: | "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"-- |
| Item Description: | Includes bibliographical references and index 2007 |
| Physical Description: | xviii, 689 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108493703 |
Staff View
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| id | DE-604.BV047104011 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:23:55Z |
| indexdate | 2024-07-10T09:02:42Z |
| institution | BVB |
| isbn | 9781108493703 |
| language | English |
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| physical | xviii, 689 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Cambridge University Press |
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| 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 Cambridge University Press 2020 xviii, 689 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index 2007 "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"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Neurowissenschaften (DE-588)7555119-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Neurosciences / Data processing Neurosciences / Statistical methods 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 |
| spellingShingle | Bielza, Concha 1966- Larrañaga, Pedro 1958- Data-driven computational neuroscience machine learning and statistical models Maschinelles Lernen (DE-588)4193754-5 gnd Neurowissenschaften (DE-588)7555119-6 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)7555119-6 (DE-588)4056995-0 |
| 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 | Maschinelles Lernen (DE-588)4193754-5 gnd Neurowissenschaften (DE-588)7555119-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Maschinelles Lernen Neurowissenschaften Statistik |
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