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Neuromorphic Cognitive Systems

A Learning and Memory Centered Approach

  • Qiang Yu
  • Huajin Tang
  • Jun Hu
  • Kay  Tan Chen

Part of the Intelligent Systems Reference Library book series (ISRL, volume 126)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 1-17
  3. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 19-41
  4. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 43-63
  5. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 89-113
  6. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 131-152
  7. Qiang Yu, Huajin Tang, Jun Hu, Kay Chen Tan
    Pages 153-172

About this book

Introduction

This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics.

The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed.

The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail.

Keywords

Cognitive Memory Intelligent Systems Neural Coding Neuromorphic Cognitive Systems Neuromorphic Computing Neuromorphic Engineering Spiking Based Learning Spiking Neural Networks

Authors and affiliations

  • Qiang Yu
    • 1
  • Huajin Tang
    • 2
  • Jun Hu
    • 3
  • Kay  Tan Chen
    • 4
  1. 1.Institute for Infocomm ResearchSingaporeSingapore
  2. 2.College of Computer ScienceSichuan UniversityChengduChina
  3. 3.AGI TechnologiesSingaporeSingapore
  4. 4.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-55310-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-55308-5
  • Online ISBN 978-3-319-55310-8
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site