Neuro-inspired information processing

Neuro-inspired information processing / Alain Cappy.

With the end of Moore's law and the emergence of new application needs such as those of the Internet of Things (IoT) or artificial intelligence (AI), neuro-inspired, or neuromorphic, information processing is attracting more and more attention from the scientific community. Its principle is to...

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Bibliographic Details
Main Author: Cappy, Alain, 1954- (Author)
Format: eBook
Language:English
Published: London : Hoboken, NJ : ISTE Ltd ; John Wiley & Sons, Inc., 2020.
Series:Electronics engineering series (London, England)
Subjects:
Neural networks (Computer science)
Neural computers.
TECHNOLOGY & ENGINEERING > Electronics > General.
Neural computers
Online Access:Click for online access
Table of Contents:
  • Cover
  • Half-Title Page
  • Dedication
  • Title Page
  • Copyright Page
  • Contents
  • Acknowledgments
  • Introduction
  • 1. Information Processing
  • 1.1. Background
  • 1.1.1. Encoding
  • 1.1.2. Memorization
  • 1.2. Information processing machines
  • 1.2.1. The Turing machine
  • 1.2.2. von Neumann architecture
  • 1.2.3. CMOS technology
  • 1.2.4. Evolution in microprocessor performance
  • 1.3. Information and energy
  • 1.3.1. Power and energy dissipated in CMOS gates and circuits
  • 1.4. Technologies of the future
  • 1.4.1. Evolution of the "binary coding/von Neumann/CMOS" system
  • 1.4.2. Revolutionary approaches
  • 1.5. Microprocessors and the brain
  • 1.5.1. Physical parameters
  • 1.5.2. Information processing
  • 1.5.3. Memorization of information
  • 1.6. Conclusion
  • 2. Information Processing in the Living
  • 2.1. The brain at a glance
  • 2.1.1. Brain functions
  • 2.1.2. Brain anatomy
  • 2.2. Cortex
  • 2.2.1. Structure
  • 2.2.2. Hierarchical organization of the cortex
  • 2.2.3. Cortical columns
  • 2.2.4. Intra- and intercolumnar connections
  • 2.3. An emblematic example: the visual cortex
  • 2.3.1. Eye and retina
  • 2.3.2. Optic nerve
  • 2.3.3. Cortex V1
  • 2.3.4. Higher level visual areas V2, V3, V4, V5 and IT
  • 2.3.5. Conclusion
  • 2.4. Conclusion
  • 3. Neurons and Synapses
  • 3.1. Background
  • 3.1.1. Neuron
  • 3.1.2. Synapses
  • 3.2. Cell membrane
  • 3.2.1. Membrane structure
  • 3.2.2. Intra- and extracellular media
  • 3.2.3. Transmembrane proteins
  • 3.3. Membrane at equilibrium
  • 3.3.1. Resting potential, Vr
  • 3.4. The membrane in dynamic state
  • 3.4.1. The Hodgkin-Huxley model
  • 3.4.2. Beyond the Hodgkin-Huxley model
  • 3.4.3. Simplified HH models
  • 3.4.4. Application of membrane models
  • 3.5. Synapses
  • 3.5.1. Biological characteristics
  • 3.5.2. Synaptic plasticity
  • 3.6. Conclusion
  • 4. Artificial Neural Networks
  • 4.1. Software neural networks
  • 4.1.1. Neuron and synapse models
  • 4.1.2. Artificial Neural Networks
  • 4.1.3. Learning
  • 4.1.4. Conclusion
  • 4.2. Hardware neural networks
  • 4.2.1. Comparison of the physics of biological systems and semiconductors
  • 4.2.2. Circuits simulating the neuron
  • 4.2.3. Circuits simulating the synapse
  • 4.2.4. Circuits for learning
  • 4.2.5. Examples of hardware neural networks
  • 4.3. Conclusion
  • References
  • Index
  • Other titles from iSTE in Electronics Engineering
  • EULA

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