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Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.
OKR Leadership -- the process for managers and leaders to practice what matters - is the secret sauce that drives transformational leadership, employee engagement and the next generation of management consulting. Join the OKR Leadership movement today with this practical guidebook from an expert business psychologist and story teller.
While the global economy languishes, one place just keeps growing despite failing banks, uncertain markets, and high unemployment: Silicon Valley. In the last two years, more than 100 incubators have popped up there, and the number of angel investors has skyrocketed. Today, 40 percent of all venture capital investments in the United States come from Silicon Valley firms, compared to 10 percent from New York. In Secrets of Silicon Valley, entrepreneur and media commentator Deborah Perry Piscione takes us inside this vibrant ecosystem where meritocracy rules the day. She explores Silicon Valley's exceptionally risk-tolerant culture, and why it thrives despite the many laws that make California one of the worst states in the union for business. Drawing on interviews with investors, entrepreneurs, and community leaders, as well as a host of case studies from Google to Paypal, Piscione argues that Silicon Valley's unique culture is the best hope for the future of American prosperity and the global business community and offers lessons from the Valley to inspire reform in other communities and industries, from Washington, DC to Wall Street.
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.
The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
"As soon as she heard me enter, Elvia awoke from a light sleep that had overcome her as she anxiously waited: 'How did it go?' Excited, I exclaimed: 'It works!' We embraced, almost overwhelmed with feelings of euphoria and happiness, aware that something epochal had happened. On that cold January night of 1971, the world's first microprocessor was born!" The creation of the microprocessor launched the digital age. The key technology allowing unprecedented integration, and the design of the world's first microprocessor, the Intel 4004, were the achievement of Federico Faggin. Shrinking an entire computer onto a tiny and inexpensive piece of silicon would come to define our daily lives, imbuing myriad devices and everyday objects with computational intelligence. In Silicon, internationally recognized inventor and entrepreneur Federico Faggin chronicles his "four lives" his formative years in war-torn Northern Italy; his pioneering work in American microelectronics; his successful career as a high-tech entrepreneur; and his more recent explorations into the mysteries of consciousness. In this heartfelt memoir, Faggin paints vivid anecdotes, steps readers through society-changing technological breakthroughs, and shares personal insights, as each of his lives propels the next.
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.