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Neuro-inspired information processing / Alain Cappy.

By: Cappy, Alain, 1954- [author.].
Material type: materialTypeLabelBookSeries: Electronics engineering series (London, England): Publisher: London : Hoboken, NJ : ISTE Ltd ; John Wiley & Sons, Inc., 2020Description: 1 online resource (245 pages) : illustrations.Content type: text | still image Media type: computer Carrier type: online resourceISBN: 9781119721802; 1119721806; 9781119721796; 1119721792.Subject(s): Neural networks (Computer science) | Neural computers | Neural computersGenre/Form: Electronic books.Additional physical formats: Print version:: Neuro-Inspired Information ProcessingDDC classification: 006.3/2 Online resources: Wiley Online Library
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
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Includes bibliographical references and index.

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

Description based on online resource; title from digital title page (viewed on May 18, 2020).

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