Neuro-inspired information processing / Alain Cappy.

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

Available to OhioLINK libraries.

Wealth is no longer just an ability to live well in a world shaped by human activities. It is also an ability to push back or defer the limits of a world in biological and climatic closure. This book examines the theoretical conflicts and the power plays which often oppose the socio-political and technical-financial practices of recognition of what intervenes in the production of this wealth - i.e. of what has value. It lays down the principles of a contributory modeling method, allowing debates around the concept of development; the building of scenarios; the negotiation of their implementation; and a cross-sectoral reading of their social, ecological and economic costs. This method, called Dynamic Modeling of Cost Systems, is based on a territorial communication device which articulates political, contractual and accounting innovations using deliberative and normative digital tools. It combines different local representations of value, in order to approach wealth through an integrated analysis of micro-, meso- and macro- issues.

About the Author
Alain Cappy is Professor Emeritus at the University of Lille, France. His research activities first concerned the design, manufacture and characterization of high frequency micro- and nanodevices. Since 2010, he has been working on neuro-inspired information processing architectures.