| Description |
1 online resource (245 pages) : illustrations |
| Series |
Electronic engineering series |
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Electronics engineering series (London, England)
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| 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 |
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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 |
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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 |
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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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Other titles from iSTE in Electronics Engineering -- EULA |
| Summary |
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 emulate in a simplified way the formidable machine to process information which is the brain, with neurons and artificial synapses organized in network. These networks can be software ' and therefore implemented in the form of a computer program ' but also hardware and produced by nanoelectronic circuits. The 'material' path allows very low energy consumption, and the possibility of faithfully reproducing the shape and dynamics of the action potentials of living neurons (biomimetic approach) or even being up to a thousand times faster (high frequency approach). This path is promising and welcomed by the major manufacturers of nanoelectronics, as circuits can now today integrate several million neurons and artificial synapses |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on May 18, 2020) |
| Subject |
Neural networks (Computer science)
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Neural computers.
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TECHNOLOGY & ENGINEERING -- Electronics -- General.
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Neural networks (Computer science)
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Neural computers.
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| Form |
Electronic book
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| ISBN |
9781119721802 |
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1119721806 |
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9781119721796 |
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1119721792 |
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