System and Circuit Design for Biologically-Inspired Intelligent Learning

System and Circuit Design for Biologically-Inspired Intelligent Learning

Turgay Temel (Bahcesehir University, Turkey)
Indexed In: SCOPUS
Release Date: October, 2010|Copyright: © 2011 |Pages: 412
DOI: 10.4018/978-1-60960-018-1
ISBN13: 9781609600181|ISBN10: 1609600185|EISBN13: 9781609600204
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Description & Coverage
Description:

Despite significant research and studies in biologically-inspired circuit design, the capability of the biological creatures still excels that of artificially human-made systems inspired in terms of adaptivity, sensitivity, and spectral characteristics.

System and Circuit Design for Biologically-Inspired Intelligent Learning aims to bridge that gap in the belief that compact organization and arrangement of the circuit and system design will be a major factor in attaining the pursued benefits of biological systems. Including research from abstract fields such as psychology to more concrete topics including circuit design, this text is designed for even fresh reader to make a smooth transition from principles to system/circuit architectures which simulate learning intelligence.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Analog Circuit Design with Fuzzy Inference
  • Biologically-inspired Learning and Intelligent System Modeling
  • DCGA Optimization Techniques
  • Implantable Neural Decoders
  • Neural Network Circuits
  • Neuromodeling and Natural Optimization
  • Parallel Hardware for Artificial Neural Networks
  • Recurrent Neural Networks
  • Representation of Neuro-information and Knowledge
  • Self-organizing Fuzzy Neural Network Training
Reviews and Testimonials

This is an excellent book. The many figures and formulas help clarify the text. ... If you are interested in system and circuit design in order to simulate intelligent learning, this is the book to have.

– Gary B Kaniuk, Psy.D., Cermak Health Services, Doody's Book Review

This book on system and circuit design for biologically-inspired intelligent learning is intended to give readers the background needed to make a successful transition from principles to system circuit architectures that simulate learning intelligence. It includes research from many diverse and abstract fields, as well as fundamental topics and current studies. Subjects addressed include: analog circuit design with fuzzy interface, biologically-inspired learning and intelligent system modeling, DCGA optimization techniques, implantable neural decoders, neural network circuits, recurrent neural networks, and representation of neuro-information and knowledge. ...

– Sci Tech Book News, BookNews.com

Biologically-inspired design is mainly concerned with thorough understanding of the behavioral and functional association of the system of interest. In the belief that compact organization and arrangement of the circuit and system design will be a major factor in attaining the pursued benefits of biological systems, the book is intended to contain fundamental topics as well as up-to-date studies for even fresh readers to make a smooth transition to system design from principles. ... We hope that the book will be a useful resource to those who wish to be familiar with intelligent systems and their implementation.

– Turgay Temel, Bahcesehir University, Turkey
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Editor/Author Biographies
Turgay Temel, received his PhD in 2002 in the area of hybrid-mode circuit design based on multi-valued logic and higher radices. In the field, he just completed a state-funded project which aims at using analog-wise information processing with digital concepts as a microarchitecture. The project was the first in this field in literature to his knowledge. His research interest diversifies, which spans to machine learning, neuroscience, biomedical engineering, sensor networks, biologically-inspired system and circuit design and high-performance circuit design. Recently, he has been focusing on reconfigurable analog systems with the hope that major units of intelligence can be emulated in an evolvable manner. The sensor development for biological information processing and bio-sensor systems have been at the core of his current scope. He has also developed unsupervised learning, i.e. clustering, techniques and currently working on single-step methods for high-performance pattern recognition.
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