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Intelligent Hybrid Systems
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume. This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems.
Nano-Net
This book constitutes the proceedings of the 4th International Conference on Nano-Networks, Nano-Net 2009, held in Lucerne, Switherland, in October 2009. The 36 invited and regular papers address the whole spectrum of Nano-Networks and spans topis like modeling, simulation, statdards, architectural aspects, novel information and graph theory aspects, device physics and interconnects, nanorobotics as well as nano-biological systems. The volume also contains the workshop on Nano-Bio-Sensing Paradigms as well as the workshop on Brain Inspired Interconnects and Circuits.
Spike-timing dependent plasticity
Hebb's postulate provided a crucial framework to understand synaptic alterations underlying learning and memory. Hebb's theory proposed that neurons that fire together, also wire together, which provided the logical framework for the strengthening of synapses. Weakening of synapses was however addressed by "not being strengthened", and it was only later that the active decrease of synaptic strength was introduced through the discovery of long-term depression caused by low frequency stimulation of the presynaptic neuron. In 1994, it was found that the precise relative timing of pre and postynaptic spikes determined not only the magnitude, but also the direction of synaptic alterations when tw...
Towards Neuromorphic Machine Intelligence
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering. In recent years, neural networks have re-emerged in relation to AI, representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering.This book represents one of the established cross-over areas where neurophysiology, cognition, and neural engineering coincide with the...
Circuits and Systems for Biomedical Applications
Circuits and Systems for Biomedical Applications-UKCAS 2018 covers several advanced topics in the area of Devices, Analog and Mixed-Signal Circuits and Systems for Biomedical Applications. The fundamental aspects of these topics are discussed, and state-of-the-art developments are presented. The book proceeds the 1st United Kingdom Circuits and Systems (UKCAS 2018) Workshop. It addresses multidisciplinary theme areas such as Biosensing, Memristors, next-generation medical diagnostics, neural-inspired circuits, neural implants, neuro-prostheses, prosthetic hand and neuro-rehabilitation. Having perceived the device and circuit assets for such technologies and knowing what challenges these pres...
Synaptic Circuits and Functions in Bio-inspired Integrated Architectures
Based upon the most advanced human-made technology on this planet, CMOS integrated circuit technology, this dissertation examines the design of hardware components and systems to establish a technological foundation for the application of future breakthroughs in the intersection of AI and neuroscience. Humans have long imagined machines, robots, and computers that learn and display intelligence akin to animals and themselves. To advance the development of these machines, specialised research in custom-built hardware designed for specific types of computation, which mirrors the structure of powerful biological nervous systems, is especially important. This dissertation is driven by the quest ...
Transactions on High-Performance Embedded Architectures and Compilers IV
Transactions on HiPEAC aims at the timely dissemination of research contributions in computer architecture and compilation methods for high-performance embedded computer systems. Recognizing the convergence of embedded and general-purpose computer systems, this journal publishes original research on systems targeted at specific computing tasks as well as systems with broad application bases. The scope of the journal therefore covers all aspects of computer architecture, code generation and compiler optimization methods of interest to researchers and practitioners designing future embedded systems. This 4th issue contains 21 papers carefully reviewed and selected out of numerous submissions a...
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Neural Information Processing
The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
