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...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2020
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| Subjects: | |
| Online Access: | BSB01 FHN01 FUBA1 Volltext |
| 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: | Title from publisher's bibliographic system (viewed on 12 Nov 2020) |
| Physical Description: | 1 Online-Ressource (xviii, 689 Seiten) |
| ISBN: | 9781108642989 |
| DOI: | 10.1017/9781108642989 |
Staff View
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| isbn | 9781108642989 |
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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, 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 |
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