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Energy-efficient devices and circuits for neuromorphic computing /

Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to this field, covering topics from neuron dynamics to energy-efficient CMOS devices and circuits. The book delves into theoretical analysis of learning processes in spiking neural networks, two-terminal ne...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Khanday, Farooq Ahmad (Editor)
Format: eBook
Language:English
Published: Amsterdam : Elsevier, 2025.
Subjects:
Metal oxide semiconductors, Complementary.
MOS complémentaires.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • ELSST476_cover.jpg_FC
  • ELSST476-FM1_LR
  • Front Matter
  • ELSST476-FM2_LR
  • Titlepage
  • ELSST476-FM3_LR
  • Copyright
  • ELSST476-FM4_LR
  • Dedication
  • ELSST476-FM5_LR
  • Contents
  • ELSST476-FM6_LR
  • Contributors
  • ELSST476-FM7_LR
  • Editor Biography
  • ELSST476-FM8_LR
  • Foreword
  • References
  • ELSST476-FM9_LR
  • Preface
  • ELSST476-FM10_LR
  • Acknowledgments
  • ELSST476-01_LR
  • Chapter 1 Spiking neural networks: Mathematical models, learning algorithms, and applications
  • 1.1 Introduction to spiking neural networks
  • 1.1.1 Overview of spiking neural networks
  • 1.1.2 Fundamental differences from traditional ANNs
  • 1.1.3 Components of SNNs
  • 1.1.4 Information processing in SNNs
  • 1.1.5 Historical context and development of SNNs
  • 1.2 Biological inspiration and neuroscience background
  • 1.2.1 Neurons and synapses
  • 1.2.2 Action potentials and spiking mechanisms
  • 1.2.3 Neural coding
  • 1.3 Mathematical models of spiking neurons
  • 1.3.1 Integrate-and-fire models
  • 1.3.2 Hodgkin-Huxley model
  • 1.3.3 Izhikevich model
  • 1.3.4 FitzHugh-Nagumo model
  • 1.3.5 Morris-Lecar model
  • 1.4 Network architectures
  • 1.4.1 Feedforward networks
  • 1.4.2 Recurrent networks
  • 1.5 Learning algorithms for SNNs
  • 1.5.1 Supervised learning methods
  • 1.5.2 Unsupervised learning methods
  • 1.5.3 Reinforcement learning in SNNs
  • 1.5.4 Hybrid learning approaches
  • 1.6 Simulation tools and frameworks
  • 1.6.1 NEURON simulator
  • 1.6.2 NEST simulator
  • 1.6.3 Brian simulator
  • 1.6.4 Nengo
  • 1.6.5 PyNN and other Python libraries
  • 1.7 Neuromorphic hardware platforms
  • 1.7.1 Intel Loihi chip
  • 1.7.2 IBM TrueNorth chip
  • 1.7.3 SpiNNaker platform
  • 1.8 Applications of SNNs
  • 1.8.1 Robotics and control systems
  • 1.8.2 Neuromorphic computing
  • 1.8.3 Pattern recognition and classification
  • 1.8.4 Cognitive computing and brain-machine interfaces
  • 1.9 Challenges and future directions
  • 1.9.1 Current challenges
  • 1.9.2 Future directions
  • 1.10 Conclusion
  • References
  • ELSST476-02_LR
  • Chapter 2 Artificial spiking neuron architectures: Devices and circuits for neuromorphic computing
  • 2.1 Introduction and background
  • 2.1.1 Historical development
  • 2.1.2 Biological inspiration
  • 2.2 Comparison with traditional computing
  • 2.2.1 Fundamental differences from von neumann architecture
  • 2.2.2 Advantages and disadvantages
  • 2.2.3 Computational models and architectures
  • 2.2.4 Performance metrics
  • 2.3 Energy efficiency in neuromorphic computing
  • 2.3.1 Comparison with traditional computing systems
  • 2.3.2 Energy-efficient techniques
  • 2.4 Review of neuromorphic computing devices
  • 2.4.1 Neuromorphic devices
  • 2.4.2 Devices-based spiking neuron
  • 2.4.3 Single-transistor neurons
  • 2.5 Circuit design for neuromorphic computing
  • 2.5.1 CMOS implementations of a spiking neuron
  • 2.6 Synaptic devices and circuits
  • 2.6.1 Synaptic plasticity and learning mechanisms
  • 2.7 Conclusion
  • References