Tools

LEADER 03404cam a22004692 4500

001 ssj0000209578;

003 WaSeSS;

005 20260712090213.0;

006 m d ;

007 cr n ;

008 020802e20021201riua es|||||||| 2|eng|d;

010 a| 2002070166;

020 a| 9780262050715;

020 a| 0262050714 (Trade Cloth)c| USD 52.00 Retail Price (Publisher)9| Out of Print;

024 3 a| 9780262050715;

035 a| (WaSeSS)ssj0000209578;

037 b| 00015994;

040 a| BIP USd| WaSeSS;

049 a| EREEa| NEHH;

050 0 a| QP363.3.E454 2002;

100 1 a| Eliasmith, Chrise| Author=| ^A884039;

245 10 a| Neural Engineeringh| [electronic resource]:b| Computation, Representation, and Dynamics in Neurobiological Systems;

260 a| Cambridge : b| MIT Press;

300 a| 326 p.b| illc| 09.000 x 07.000 in.;

440 0 a| Computational Neuroscience Ser.;

506 a| Available only to authorized users.;

520 8 a| Annotationb| For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In<i>Neural Engineering</i>, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.<br /><br />The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.;

538 a| Mode of access: World Wide Web;

650 4 a| Neural Networks (Neurobiology);

650 4 a| Neural Networks (Computer Science);

655 0 a| Electronic books.=| ^A491897;

700 1 a| Anderson, Charles H.e| Author=| ^A1182846;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=78178&scope=site&custid=s5822723&authtype=ip,shib;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 5700932;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5700932-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 9/19/2021x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5700932-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 9/19/2021x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5700932-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 9/19/2021x| EBOOKz| JERESOURCE;

Neural Engineering Computation, Representation, and Dynamics in Neurobiological Systems

Author/creator	Eliasmith, Chris Author
Other author	Anderson, Charles H. Author
Format	Electronic
Publication Info	Cambridge : MIT Press
Description	326 p. ill 09.000 x 07.000 in.
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Neural Networks (Neurobiology) Neural Networks (Computer Science)

Series	Computational Neuroscience Ser.
Summary	Annotation For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In<i>Neural Engineering</i>, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.<br /><br />The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2002070166
ISBN	9780262050715
ISBN	0262050714 (Trade Cloth) Out of Print
Standard identifier#	9780262050715
Stock number	00015994

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Neural Engineering Computation, Representation, and Dynamics in Neurobiological Systems

Click here for more information about this title

Availability