Advances in Neural Computation, Machine Learning, and Cognitive Research

Selected Papers from the XIX International Conference on Neuroinformatics, October 2-6, 2017, Moscow, Russia

  • Boris Kryzhanovsky
  • Witali Dunin-Barkowski
  • Vladimir Redko
Conference proceedings NEUROINFORMATICS 2017

Part of the Studies in Computational Intelligence book series (SCI, volume 736)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Neural Network Theory

    1. Front Matter
      Pages 1-1
    2. Yuriy S. Fedorenko, Yuriy E. Gapanyuk, Svetlana V. Minakova
      Pages 3-8
    3. Tatiana Lazovskaya, Dmitry Tarkhov, Alexander Vasilyev
      Pages 17-22
    4. Yury S. Prostov, Yury V. Tiumentsev
      Pages 33-38
  3. Applications of Neural Networks

    1. Front Matter
      Pages 45-45
    2. Aleksandr Bakhshiev, Lev Stankevich
      Pages 47-52
    3. Ivan Fomin, Dmitrii Gromoshinskii, Aleksandr Bakhshiev
      Pages 79-84
    4. Ivan Fomin, Viktor Mikhailov, Aleksandr Bakhshiev, Natalia Merkulyeva, Aleksandr Veshchitskii, Pavel Musienko
      Pages 85-90
    5. Vasiliy Gai, Pavel Rodionov, Maxim Derbasov, Dmitriy Lyakhmanov, Alla Koshurina
      Pages 98-103
    6. S. I. Malafeev, S. S. Malafeev, Y. V. Tikhonov
      Pages 110-116

About these proceedings

Introduction

This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.

Keywords

Neural Excitability Cellular Mechanisms Cognition and Behavior Learning and Memory Motivation and Emotion Bayesian Networks Kernel Methods Generative Models Deep Learning Networks Memristor Based Neural Networks Random Neural Networks Modeling Dynamical Systems Brain Reverse Engineering Biomedical Signal Processing Modeling of Cognitive Evolution Adaptive Algorithms

Editors and affiliations

  • Boris Kryzhanovsky
    • 1
  • Witali Dunin-Barkowski
    • 2
  • Vladimir Redko
    • 3
  1. 1.Scientific Research Institute for System AnalysisRussian Academy of SciencesMoscowRussia
  2. 2.Scientific Research Institute for System AnalysisRussian Academy of SciencesMoscowRussia
  3. 3.Scientific Research Institute for System AnalysisRussian Academy of SciencesMoscowRussia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-66604-4
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-66603-7
  • Online ISBN 978-3-319-66604-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book