登入選單
返回Google圖書搜尋
Data-Driven Computational Neuroscience
Concha BielzaPedro Larrañaga
其他書名
Machine Learning and Statistical Models
出版Cambridge University Press, 2020-11-26
主題Computers / Artificial Intelligence / GeneralComputers / Artificial Intelligence / Computer Vision & Pattern RecognitionComputers / Data Science / Neural NetworksMedical / NeurosciencePsychology / Cognitive Psychology & CognitionScience / Life Sciences / Anatomy & PhysiologyScience / Life Sciences / NeuroscienceTechnology & Engineering / Signals & Signal Processing
ISBN110849370X9781108493703
URLhttp://books.google.com.hk/books?id=l9sCEAAAQBAJ&hl=&source=gbs_api
EBookSAMPLE
註釋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.