Neuromorphic Cognitive Systems: A Learning and Memory Centered Approach (Intelligent Systems Reference Library)
暫譯: 神經形態認知系統:以學習和記憶為中心的方法(智能系統參考文獻庫)

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

  • 出版商: Springer
  • 出版日期: 2017-05-10
  • 售價: $6,780
  • 貴賓價: 9.5$6,441
  • 語言: 英文
  • 頁數: 172
  • 裝訂: Hardcover
  • ISBN: 3319553089
  • ISBN-13: 9783319553085
  • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

本書從以學習和記憶為中心的角度介紹神經形態認知系統。它說明了如何構建神經元系統網絡,以執行基於脈衝的資訊處理、計算和高階認知任務。這對於廣泛的讀者群體都有益,包括對神經形態計算和神經形態工程感興趣的本科生和研究生,以及參與神經形態認知系統、神經形態感測器和處理器以及認知機器人設計和應用的工程師和專業人士。

本書建立了一個系統性的框架,從基於脈衝的神經編碼的基本數學和計算方法,到單層和多層網絡中的學習,再到由記憶和認知組成的接近認知水平。由於整合脈衝神經元以形成類似大腦的認知功能的機制尚不清楚,因此迫切需要對神經形態認知系統的研究。

本書涵蓋的主題從神經元層面到系統層面。在神經元層面,突觸適應在學習模式中扮演著重要角色。為了執行更高階的認知功能,如識別和記憶,具有學習能力的脈衝神經元被持續整合,構建一個具備編碼、學習和記憶功能的系統。本書詳細描述了這些方面。