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Reconfigurable Cellular Neural Networks and Their Applications

  • Müştak E. Yalçın
  • Tuba Ayhan
  • Ramazan Yeniçeri

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Nonlinear Circuits book sub series (BRIEFSNONLINCIRC)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Müştak E. Yalçın, Tuba Ayhan, Ramazan Yeniçeri
    Pages 1-3
  3. Müştak E. Yalçın, Tuba Ayhan, Ramazan Yeniçeri
    Pages 5-22
  4. Müştak E. Yalçın, Tuba Ayhan, Ramazan Yeniçeri
    Pages 23-50
  5. Müştak E. Yalçın, Tuba Ayhan, Ramazan Yeniçeri
    Pages 51-71
  6. Back Matter
    Pages 73-74

About this book

Introduction

This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology.

The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors’ neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification.


Keywords

Neural Mass Models Wilson–Cowan Population Model Olfactory System Excitatory and Inhibitory Neurons Neuronal Populations Random Reconfiguration Artificial Antennal Lobe

Authors and affiliations

  • Müştak E. Yalçın
    • 1
  • Tuba Ayhan
    • 2
  • Ramazan Yeniçeri
    • 3
  1. 1.Department of Electronics and Telecommunications EngineeringIstanbul Technical UniversityIstanbulTurkey
  2. 2.Department of Electronics and Telecommunications EngineeringIstanbul Technical UniversityIstanbulTurkey
  3. 3.Aeronautical EngineeringIstanbul Technical UniversityIstanbulTurkey

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-17840-6
  • Copyright Information The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Intelligent Technologies and Robotics
  • Print ISBN 978-3-030-17839-0
  • Online ISBN 978-3-030-17840-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site