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Neuromorphic Engineering - The practice of using electrical analog circuitry systems to imitate neuro-biological structures that are present in the nervous system - cover

Neuromorphic Engineering - The practice of using electrical analog circuitry systems to imitate neuro-biological structures that are present in the nervous system

Fouad Sabry

Casa editrice: One Billion Knowledgeable

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Sinossi

What Is Neuromorphic Engineering
 
Neuromorphic computing and neuromorphic engineering are both terms that refer to the same thing: the use of very-large-scale integration (VLSI) systems that incorporate electrical analog circuits to simulate neuro-biological structures that are found in the nervous system. Any electronic device that does calculations with the help of artificial neurons that are implemented as physical structures is referred to as a neuromorphic computer or chip. Recently, the word "neuromorphic" has been used to refer to analog, digital, mixed-mode analog/digital VLSI, and software systems that embody models of brain systems. This use of the term has become more common. To actualize the implementation of neuromorphic computing on the hardware level, oxide-based memristors, spintronic memory, threshold switches, and transistors are some of the components that may be used. Training software-based neuromorphic systems of spiking neural networks can be accomplished through the use of error backpropagation, for instance through the utilization of Python-based frameworks like snnTorch, or through the utilization of canonical learning rules from the biological learning literature, for instance through the utilization of BindsNet.
 
How You Will Benefit
 
(I) Insights, and validations about the following topics:
 
Chapter 1: Neuromorphic engineering
 
Chapter 2: Artificial neuron
 
Chapter 3: Bio-inspired computing
 
Chapter 4: Steve Furber
 
Chapter 5: Carver Mead
 
Chapter 6: Recurrent neural network
 
Chapter 7: Neural network
 
Chapter 8: Wetware computer
 
Chapter 9: Computational neurogenetic modeling
 
Chapter 10: Spiking neural network
 
Chapter 11: Neurorobotics
 
Chapter 12: Misha Mahowald
 
Chapter 13: Memristor
 
Chapter 14: Physical neural network
 
Chapter 15: NOMFET
 
Chapter 16: Massimiliano Versace
 
Chapter 17: Kwabena Boahen
 
Chapter 18: SpiNNaker
 
Chapter 19: Cognitive computer
 
Chapter 20: Glossary of artificial intelligence
 
Chapter 21: Hai Li
 
(II) Answering the public top questions about neuromorphic engineering.
 
(III) Real world examples for the usage of neuromorphic engineering in many fields.
 
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neuromorphic engineering' technologies.
 
Who This Book Is For
 
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuromorphic engineering.
Disponibile da: 10/07/2022.
Lunghezza di stampa: 382 pagine.

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