Neuro-inspired information processing /

Mostrar otras versiones (1)

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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Cappy, Alain, 1954- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Hoboken, NJ : ISTE Ltd ; John Wiley & Sons, Inc., 2020.
Colección:Electronics engineering series (London, England)
Temas:
Neural networks (Computer science)
Neural computers.
Réseaux neuronaux (Informatique)
Ordinateurs neuronaux.
TECHNOLOGY & ENGINEERING > Electronics > General.
Neural computers
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

Ejemplares similares