000			01811 a2200217 4500
003			OSt
005			20211220100057.0
008			211220b xxu||||| |||| 00| 0 eng d
020			_a9781108493703
040			_cIIT Kanpur
041			_aeng
082			_a612.8 _bB476d
100			_aBielza, Concha
245			_aData-driven computational neuroscience _bmachine learning and statistical models _cConcha Bielza and Pedro Larranaga
260			_bCambridge University Press _c2021 _aCambridge
300			_axviii, 689p
520			_aData-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			_aNeurosciences -- Data processing
700			_aLarranaga, Pedro
942			_cBK
999			_c565034 _d565034