Neural Engineering

Neural Engineering
Author :
Publisher : MIT Press
Total Pages : 384
Release :
ISBN-10 : 0262550601
ISBN-13 : 9780262550604
Rating : 4/5 (604 Downloads)

Book Synopsis Neural Engineering by : Chris Eliasmith

Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.


Neural Engineering Related Books

Neural Engineering
Language: en
Pages: 384
Authors: Chris Eliasmith
Categories: Computers
Type: BOOK - Published: 2003 - Publisher: MIT Press

DOWNLOAD EBOOK

A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Neural Smithing
Language: en
Pages: 359
Authors: Russell Reed
Categories: Computers
Type: BOOK - Published: 1999-02-17 - Publisher: MIT Press

DOWNLOAD EBOOK

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. T
Neural Network Learning and Expert Systems
Language: en
Pages: 392
Authors: Stephen I. Gallant
Categories: Computers
Type: BOOK - Published: 1993 - Publisher: MIT Press

DOWNLOAD EBOOK

presents a unified and in-depth development of neural network learning algorithms and neural network expert systems
Pattern Recognition by Self-organizing Neural Networks
Language: en
Pages: 724
Authors: Gail A. Carpenter
Categories: Computers
Type: BOOK - Published: 1991 - Publisher: MIT Press

DOWNLOAD EBOOK

Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields
Neural Networks for Pattern Recognition
Language: en
Pages: 450
Authors: Albert Nigrin
Categories: Computers
Type: BOOK - Published: 1993 - Publisher: MIT Press

DOWNLOAD EBOOK

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Network