Fundamentals of Neural Network Modeling - Info and Reading Options
Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)
By Daniel S. Levine
"Fundamentals of Neural Network Modeling" is published by The MIT Press in December 11, 1998 - Cambridge, Mass, it has 428 pages and the language of the book is English.
“Fundamentals of Neural Network Modeling” Metadata:
- Title: ➤ Fundamentals of Neural Network Modeling
- Author: Daniel S. Levine
- Language: English
- Number of Pages: 428
- Publisher: The MIT Press
- Publish Date: December 11, 1998
- Publish Location: Cambridge, Mass
“Fundamentals of Neural Network Modeling” Subjects and Themes:
- Subjects: ➤ Neurological Models - Neuropsychology - Cognitive neuroscience - Cognition - Neural networks (Neurobiology) - Neuropsychiatry - Neural Networks (Computer) - Dementia - Neuropsychiatrie - Dementie - Human Anatomy & Physiology - Aandacht - MEDICAL - Neurowetenschappen - Neurosciences cognitives - Computer Neural Networks - PSYCHOLOGY - Neuropsychological Tests - Health & Biological Sciences - Cognitie - Neuroscience - Neuropsychologie - Neurale netwerken - Reseaux neuronaux (Neurobiologie) - Neural networks (computer science) - Réseaux neuronaux (Neurobiologie) - Models, Neurological
Edition Specifications:
- Format: Hardcover
- Weight: 2.4 pounds
- Dimensions: 10.1 x 6.6 x 1 inches
Edition Identifiers:
- The Open Library ID: OL10237473M - OL16918615W
- Online Computer Library Center (OCLC) ID: 37553788 - 43476111
- Library of Congress Control Number (LCCN): 97034792
- ISBN-13: 9780262161756
- ISBN-10: 0262161753
- All ISBNs: 0262161753 - 9780262161756
AI-generated Review of “Fundamentals of Neural Network Modeling”:
"Fundamentals of Neural Network Modeling" Description:
The Open Library:
Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics.
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