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Neuro Inspired Information Processing


Neuro Inspired Information Processing
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Neuro Inspired Information Processing


Neuro Inspired Information Processing
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Author : Alain Cappy
language : en
Publisher: John Wiley & Sons
Release Date : 2020-03-31

Neuro Inspired Information Processing written by Alain Cappy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-31 with Technology & Engineering categories.


With the end of Moore's law and the emergence of new application needs such as those of the Internet of Things (IoT) or artificial intelligence (AI), neuro-inspired, or neuromorphic, information processing is attracting more and more attention from the scientific community. Its principle is to emulate in a simplified way the formidable machine to process information which is the brain, with neurons and artificial synapses organized in network. These networks can be software and therefore implemented in the form of a computer program but also hardware and produced by nanoelectronic circuits. The material path allows very low energy consumption, and the possibility of faithfully reproducing the shape and dynamics of the action potentials of living neurons (biomimetic approach) or even being up to a thousand times faster (high frequency approach). This path is promising and welcomed by the major manufacturers of nanoelectronics, as circuits can now today integrate several million neurons and artificial synapses.



Neural Information Processing And Vlsi


Neural Information Processing And Vlsi
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Author : Bing J. Sheu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Neural Information Processing And Vlsi written by Bing J. Sheu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.


Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has beenespecially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.



Artificial Neural Networks As Models Of Neural Information Processing


Artificial Neural Networks As Models Of Neural Information Processing
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Author : Marcel van Gerven
language : en
Publisher: Frontiers Media SA
Release Date : 2018-02-01

Artificial Neural Networks As Models Of Neural Information Processing written by Marcel van Gerven and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-01 with categories.


Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.



Time Space Spiking Neural Networks And Brain Inspired Artificial Intelligence


Time Space Spiking Neural Networks And Brain Inspired Artificial Intelligence
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Author : Nikola K. Kasabov
language : en
Publisher: Springer
Release Date : 2018-08-29

Time Space Spiking Neural Networks And Brain Inspired Artificial Intelligence written by Nikola K. Kasabov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-29 with Technology & Engineering categories.


Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.



Neural Information Processing


Neural Information Processing
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Author : Akira Hirose
language : en
Publisher: Springer
Release Date : 2016-09-30

Neural Information Processing written by Akira Hirose and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-30 with Computers categories.


The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.



Advances In Neural Information Processing Systems 7


Advances In Neural Information Processing Systems 7
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Author : Gerald Tesauro
language : en
Publisher: MIT Press
Release Date : 1995

Advances In Neural Information Processing Systems 7 written by Gerald Tesauro and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.


November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Visual Processing, and Applications. Topics of special interest include the analysis of recurrent nets, connections to HMMs and the EM procedure, and reinforcement- learning algorithms and the relation to dynamic programming. On the theoretical front, progress is reported in the theory of generalization, regularization, combining multiple models, and active learning. Neuroscientific studies range from the large-scale systems such as visual cortex to single-cell electrotonic structure, and work in cognitive scientific is closely tied to underlying neural constraints. There are also many novel applications such as tokamak plasma control, Glove-Talk, and hand tracking, and a variety of hardware implementations, with particular focus on analog VLSI.



Biophysics Of Computation


Biophysics Of Computation
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Author : Christof Koch
language : en
Publisher: Oxford University Press
Release Date : 2004-10-28

Biophysics Of Computation written by Christof Koch and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-28 with Medical categories.


Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.



Advances In Neural Information Processing Systems 17


Advances In Neural Information Processing Systems 17
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Author : Lawrence K. Saul
language : en
Publisher: MIT Press
Release Date : 2005

Advances In Neural Information Processing Systems 17 written by Lawrence K. Saul and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.


Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.



Dynamic Interactions In Neural Networks Models And Data


Dynamic Interactions In Neural Networks Models And Data
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Author : Michael A. Arbib
language : en
Publisher: Springer Science & Business Media
Release Date : 1989

Dynamic Interactions In Neural Networks Models And Data written by Michael A. Arbib and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Computers categories.


The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.



Federated Learning


Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25

Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.


This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”