Neuromorphic Engineering
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Neuromorphic Systems Engineering
Author: Tor Sverre Lande
language: en
Publisher: Springer Science & Business Media
Release Date: 1998-04-30
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include: large scale analog systems in silicon neuromorphic silicon auditory (ear) and vision (eye) systems in silicon learning and adaptation in silicon merging biology and technology micropower analog circuit design analog memory analog interchipcommunication on digital buses £/LIST£ Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Neuromorphic Engineering
Author: Fouad Sabry
language: en
Publisher: One Billion Knowledgeable
Release Date: 2022-07-10
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.
Neuromorphic Engineering Systems and Applications
Author: André van Schaik
language: en
Publisher: Frontiers Media SA
Release Date: 2015-07-05
Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades neuromorphic engineers focused on building models of sensors, such as silicon cochleas and retinas, and building blocks such as silicon neurons and synapses. These designs have honed our skills in implementing sensors and neural networks in VLSI using analog and mixed mode circuits. Over the last decade the address event representation has been used to interface devices and computers from different designers and even different groups. This facility has been essential for our ability to combine sensors, neural networks, and actuators into neuromorphic systems. More recently, several big projects have emerged to build very large scale neuromorphic systems. The Telluride Neuromorphic Engineering Workshop (since 1994) and the CapoCaccia Cognitive Neuromorphic Engineering Workshop (since 2009) have been instrumental not only in creating a strongly connected research community, but also in introducing different groups to each other’s hardware. Many neuromorphic systems are first created at one of these workshops. With this special research topic, we showcase the state-of-the-art in neuromorphic systems.