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Neuromorphic Systems
Neuromorphic systems are implementations in silicon of sensory and neural systems whose architecture and design are based on neurobiology. This growing area proffers exciting possibilities, such as sensory systems that can compete with human senses and pattern recognition systems that can run in real time. The area is at the intersection of neurophysiology, computer science and electrical engineering. This book brings together recent developments in Europe and the US, so that researchers in both academia and industry can find out about the state of the art. As well as elementary material on what neuromorphic systems are and why they are growing in importance, the book contains details of current work. Them are articles on aspects of implementing sensory neuromorphic systems, as well as articles on neuromorphic hardware.
Smart Adaptive Systems on Silicon
Intelligent/smart systems have become common practice in many engineering applications. On the other hand, current low cost standard CMOS technology (and future foreseeable developments) makes available enormous potentialities. The next breakthrough will be the design and development of "smart adaptive systems on silicon" i.e. very power and highly size efficient complete systems (i.e. sensing, computing and "actuating" actions) with intelligence on board on a single silicon die. Smart adaptive systems on silicon will be able to "adapt" autonomously to the changing environment and will be able to implement "intelligent" behaviour and both perceptual and cognitive tasks. At last, they will co...
Neural Nets WIRN Vietri-01
This volume contains the proceedings of the 12th Italian Workshop on Neural Nets WIRN VIETRI-Ol, jointly organized by the International Institute for Advanced Scientific Studies "Eduardo R. Caianiello" (IIASS), the Societa Italiana Reti Neuroniche (SIREN), the IEEE NNC Italian RIG and the Italian SIG of the INNS. Following the tradition of previous years, we invited three foreign scientists to the workshop, Dr. G. Indiveri and Professors A. Roy and R. Sun, who respectively presented the lectures "Computation in Neuromorphic Analog VLSI Systems", "On Connectionism and Rule Extraction", "Beyond Simple Rule Extraction: Acquiring Planning Knowledge from Neural Networks" (the last two papers bein...
Bio-Inspired Systems: Computational and Ambient Intelligence
This book constitutes the refereed proceedings of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, held in Salamanca, Spain in June 2009. The 167 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 230 submissions. The papers are organized in thematic sections on theoretical foundations and models; learning and adaptation; self-organizing networks, methods and applications; fuzzy systems; evolutionary computation and genetic algoritms; pattern recognition; formal languages in linguistics; agents and multi-agent on intelligent systems; brain-computer interfaces (bci); multiobjetive optimization; robotics; bioinformatics; biomedical applications; ambient assisted living (aal) and ambient intelligence (ai); other applications.
Synaptic Plasticity for Neuromorphic Systems
One of the most striking properties of biological systems is their ability to learn and adapt to ever changing environmental conditions, tasks and stimuli. It emerges from a number of different forms of plasticity, that change the properties of the computing substrate, mainly acting on the modification of the strength of synaptic connections that gate the flow of information across neurons. Plasticity is an essential ingredient for building artificial autonomous cognitive agents that can learn to reliably and meaningfully interact with the real world. For this reason, the neuromorphic community at large has put substantial effort in the design of different forms of plasticity and in putting them to practical use. These plasticity forms comprise, among others, Short Term Depression and Facilitation, Homeostasis, Spike Frequency Adaptation and diverse forms of Hebbian learning (e.g. Spike Timing Dependent Plasticity). This special research topic collects the most advanced developments in the design of the diverse forms of plasticity, from the single circuit to the system level, as well as their exploitation in the implementation of cognitive systems.
Mobile Health
This book offers a comprehensive report on the technological aspects of Mobile Health (mHealth) and discusses the main challenges and future directions in the field. It is divided into eight parts: (1) preventive and curative medicine; (2) remote health monitoring; (3) interoperability; (4) framework, architecture, and software/hardware systems; (5) cloud applications; (6) radio technologies and applications; (7) communication networks and systems; and (8) security and privacy mechanisms. The first two parts cover sensor-based and bedside systems for remotely monitoring patients’ health condition, which aim at preventing the development of health problems and managing the prognosis of acut...
Neuromorphic Systems Engineering
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 Networ...
Nature-Inspired VLSI Circuits - From Concept to Implementation
Nature-inspired VLSI circuit technology offers unique approach for studying, analyzing, designing, and implementing VLSI circuits through perception, reasoning and action mimicking the nature. Such circuit technology covers various aspects of nature-inspired VLSI circuit design techniques, such as the design rule bases, design principles, computing and information processing algorithms, sensing and interfacing techniques, energy harvesting and power management.
VLSI for Neural Networks and Artificial Intelligence
Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development o...
