© 2011

Perception-Action Cycle

Models, Architectures, and Hardware

  • Vassilis Cutsuridis
  • Amir Hussain
  • John G. Taylor

Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Computational Neuroscience Models

    1. Front Matter
      Pages 1-3
    2. John K. Tsotsos, Albert L. Rothenstein
      Pages 5-21
    3. Mauro Ursino, Elisa Magosso, Cristiano Cuppini
      Pages 23-62
    4. Daniel S. Levine
      Pages 135-168
    5. Rodolphe J. Gentili, Hyuk Oh, Javier Molina, José L. Contreras-Vidal
      Pages 187-217
    6. Ryunosuke Nishimoto, Jun Tani
      Pages 219-241
    7. John G. Taylor
      Pages 335-357
  3. Cognitive Architectures

    1. Front Matter
      Pages 359-361
    2. Konstantinos Rapantzikos, Yannis Avrithis, Stefanos Kolias
      Pages 363-386
    3. John N. Karigiannis, Theodoros Rekatsinas, Costas S. Tzafestas
      Pages 497-538
    4. Vishwanathan Mohan, Pietro Morasso, Giorgio Metta, Stathis Kasderidis
      Pages 539-572

About this book


The perception-action cycle is the circular flow of information that takes place between the organism and its environment in the course of a sensory-guided sequence of behavior towards a goal. Each action causes changes in the environment that are analyzed bottom-up through the perceptual hierarchy and lead to the processing of further action, and top-down through the executive hierarchy toward motor effectors. These actions cause new changes that are analyzed and lead to new action, and so the cycle continues.

The Perception-Action cycle: Models, Architectures and Hardware book provides focused and easily accessible reviews of various aspects of the perception-action cycle. It is an unparalleled resource of information that will be an invaluable companion to anyone in constructing and developing models, algorithms, and hardware implementations of autonomous machines empowered with cognitive capabilities.

The book is divided into three main parts. In the first part, leading computational neuroscientists present brain-inspired models of perception, attention, cognitive control, decision making, conflict resolution and monitoring, knowledge representation and reasoning, learning and memory, planning and action, and consciousness grounded in experimental data. In the second part, architectures, algorithms, and systems with cognitive capabilities and minimal guidance from the brain are discussed. These architectures, algorithms, and systems are inspired by cognitive science, computer vision, robotics, information theory, machine learning, computer agents, and artificial intelligence. In the third part, the analysis, design, and implementation of hardware systems with robust cognitive abilities from the areas of mechatronics, sensing technology, sensor fusion, smart sensor networks, control rules, controllability, stability, model/knowledge representation, and reasoning are discussed.

About the Editors:
Vassilis Cutsuridis is a Senior Research Scientist at the Center for Memory and Brain at Boston University, Boston, USA.
Amir Hussain is a Reader in Computing Science in the Department of Computing Science and Mathematics at the University of Stirling, UK.
John G. Taylor is an Emeritus Distinguished Professor of Mathematics in the Department of Mathematics at King’s College, London, UK.

Editors and affiliations

  • Vassilis Cutsuridis
    • 1
  • Amir Hussain
    • 2
  • John G. Taylor
    • 3
  1. 1.Department of PsychologyBoston UniversityBostonUSA
  2. 2.Dept. Computing ScienceUniversity of StirlingStirlingUnited Kingdom
  3. 3.King's College London, Dept. MathematicsUniversity of LondonLondonUnited Kingdom

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