國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Dynamic neurosciencestatistics, mode...

Chen, Zhe.

Dynamic neurosciencestatistics, modeling, and control /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic neuroscienceedited by Zhe Chen, Sridevi V. Sarma.
其他題名:	statistics, modeling, and control /
其他作者:	Chen, Zhe.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xxi, 328 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	NeurosciencesResearch.
電子資源:	http://dx.doi.org/10.1007/978-3-319-71976-4
ISBN:	9783319719764$q(electronic bk.)

Dynamic neurosciencestatistics, modeling, and control /
Dynamic neurosciencestatistics, modeling, and control /[electronic resource] :edited by Zhe Chen, Sridevi V. Sarma. - Cham :Springer International Publishing :2018. - xxi, 328 p. :ill., digital ;24 cm.

This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers. Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis; Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience; Covers a wide range of representative case studies in neuroscience applications.

ISBN: 9783319719764$q(electronic bk.)

Standard No.: 10.1007/978-3-319-71976-4doiSubjects--Topical Terms:

520764
Neurosciences
--Research.

LC Class. No.: RC337

Dewey Class. No.: 616.80072

Dynamic neurosciencestatistics, modeling, and control /
LDR:02198nmm a2200301 a 4500 001 529972
003 DE-He213
005 20180808093749.0
006 m d
007 cr nn 008maaau
008 181107s2018 gw s 0 eng d
020 $a 9783319719764$q(electronic bk.)
020 $a 9783319719757$q(paper)
024 7 $a 10.1007/978-3-319-71976-4 $2 doi
035 $a 978-3-319-71976-4
040 $a GP $c GP
041 0 $a eng
050 4 $a RC337
072 7 $a MQW $2 bicssc
072 7 $a TEC009000 $2 bisacsh
082 0 4 $a 616.80072 $2 23
090 $a RC337 $b .D997 2018
245 0 0 $a Dynamic neuroscience $h [electronic resource] : $b statistics, modeling, and control / $c edited by Zhe Chen, Sridevi V. Sarma.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xxi, 328 p. : $b ill., digital ; $c 24 cm.
520 $a This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers. Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis; Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience; Covers a wide range of representative case studies in neuroscience applications.
650 0 $a Neurosciences $x Research. $3 520764
650 0 $a Neurosciences $x Data processing $x Research. $3 803475
650 0 $a Numerical analysis $x Research. $3 230302
650 1 4 $a Engineering. $3 210888
650 2 4 $a Biomedical Engineering. $3 190464
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
650 2 4 $a Neurosciences. $3 211508
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
700 1 $a Chen, Zhe. $3 803473
700 1 $a Sarma, Sridevi V. $3 803474
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-71976-4
950 $a Engineering (Springer-11647)