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Neuromorphic cognitive systems = a l...

Yu, Qiang.

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Neuromorphic cognitive systems = a learning and memory centered approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neuromorphic cognitive systems/ by Qiang Yu ... [et al.].
Reminder of title:	a learning and memory centered approach /
other author:	Yu, Qiang.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xiv, 172 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- A Spike-Timing Based Integrated Model for Pattern Recognition -- Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns -- A Spiking Neural Network System for Robust Sequence Recognition -- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- A Hierarchically Organized Memory Model with Temporal Population Coding -- Spiking Neuron Based Cognitive Memory Model.
Contained By:	Springer eBooks
Subject:	Computational neuroscience. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-55310-8
ISBN:	9783319553108

Neuromorphic cognitive systems = a learning and memory centered approach /
Neuromorphic cognitive systemsa learning and memory centered approach /[electronic resource] :by Qiang Yu ... [et al.]. - Cham :Springer International Publishing :2017. - xiv, 172 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1261868-4394 ;. - Intelligent systems reference library ;v.126..

Introduction -- Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- A Spike-Timing Based Integrated Model for Pattern Recognition -- Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns -- A Spiking Neural Network System for Robust Sequence Recognition -- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- A Hierarchically Organized Memory Model with Temporal Population Coding -- Spiking Neuron Based Cognitive Memory Model.

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.

ISBN: 9783319553108

Standard No.: 10.1007/978-3-319-55310-8doiSubjects--Topical Terms:

610819
Computational neuroscience.

LC Class. No.: QP357.5

Dewey Class. No.: 612.8233

Neuromorphic cognitive systems = a learning and memory centered approach /
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245 0 0 $a Neuromorphic cognitive systems $h [electronic resource] : $b a learning and memory centered approach / $c by Qiang Yu ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xiv, 172 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.126
505 0 $a Introduction -- Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- A Spike-Timing Based Integrated Model for Pattern Recognition -- Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns -- A Spiking Neural Network System for Robust Sequence Recognition -- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- A Hierarchically Organized Memory Model with Temporal Population Coding -- Spiking Neuron Based Cognitive Memory Model.
520 $a 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.
650 0 $a Computational neuroscience. $3 610819
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Neurosciences. $3 588700
700 1 $a Yu, Qiang. $3 1035617
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.126. $3 3242230
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-55310-8
950 $a Engineering (Springer-11647)