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Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Overview of attention for book
Cover of 'Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices'

Table of Contents

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    Book Overview
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    Chapter 1 Hardware Spiking Artificial Neurons, Their Response Function, and Noises
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    Chapter 2 Synaptic Plasticity with Memristive Nanodevices
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    Chapter 3 Neuromemristive Systems: A Circuit Design Perspective
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    Chapter 4 Memristor-Based Platforms: A Comparison Between Continous-Time and Discrete-Time Cellular Neural Networks
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    Chapter 5 Reinterpretation of Magnetic Tunnel Junctions as Stochastic Memristive Devices
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    Chapter 6 Multiple Binary OxRAMs as Synapses for Convolutional Neural Networks
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    Chapter 7 Nonvolatile Memory Crossbar Arrays for Non-von Neumann Computing
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    Chapter 8 Novel Biomimetic Si Devices for Neuromorphic Computing Architecture
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    Chapter 9 Exploiting Variability in Resistive Memory Devices for Cognitive Systems
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    Chapter 10 Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices
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    Chapter 11 Erratum to: Novel Biomimetic Si Devices for Neuromorphic Computing Architecture
Attention for Chapter 9: Exploiting Variability in Resistive Memory Devices for Cognitive Systems
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Chapter title
Exploiting Variability in Resistive Memory Devices for Cognitive Systems
Chapter number 9
Book title
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Published by
Springer, New Delhi, January 2017
DOI 10.1007/978-81-322-3703-7_9
Book ISBNs
978-8-13-223701-3, 978-8-13-223703-7
Authors

Vivek Parmar, Manan Suri

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Librarian 1 25%
Researcher 1 25%
Readers by discipline Count As %
Engineering 3 75%
Computer Science 1 25%

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