Neuronal Network Research Horizons

Neuronal Network Research Horizons PDF

Author: Martin L. Weiss

Publisher: Nova Publishers

Published: 2007

Total Pages: 350

ISBN-13: 9781600214851

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In neuroscience, a neural network is a bit of conceptual juggernaut: the conceptual transition from neuroanatomy, a rigorously descriptive discipline of observed structure, to the designation of the parameters delimiting a 'network' can be problematic. In outline a neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognisable circuit. Communication between neurons often involves an electrochemical process. The interface through which they interact with surrounding neurons usually consists of several dendrites (input connections), which are connected via synapses to other neurons, and one axon (output connection). If the sum of the input signals surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon. This important book presents the latest research in this field.

Advances in Neural Network Research and Applications

Advances in Neural Network Research and Applications PDF

Author: Zhigang Zeng

Publisher: Springer Science & Business Media

Published: 2010-05-10

Total Pages: 921

ISBN-13: 3642129900

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This book is a part of the Proceedings of the Seventh International Symposium on Neural Networks (ISNN 2010), held on June 6-9, 2010 in Shanghai, China. Over the past few years, ISNN has matured into a well-established premier international symposium on neural networks and related fields, with a successful sequence of ISNN series in Dalian (2004), Chongqing (2005), Chengdu (2006), Nanjing (2007), Beijing (2008), and Wuhan (2009). Following the tradition of ISNN series, ISNN 2010 provided a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research in neural networks and related fields, and also discuss the major opportunities and challenges of future neural network research. Over the past decades, the neural network community has witnessed significant breakthroughs and developments from all aspects of neural network research, including theoretical foundations, architectures, and network organizations, modeling and simulation, empirical studies, as well as a wide range of applications across different domains. The recent developments of science and technology, including neuroscience, computer science, cognitive science, nano-technologies and engineering design, among others, has provided significant new understandings and technological solutions to move the neural network research toward the development of complex, large scale, and networked brain-like intelligent systems. This long-term goals can only be achieved with the continuous efforts from the community to seriously investigate various issues on neural networks and related topics.

Proceedings of the 22nd Engineering Applications of Neural Networks Conference

Proceedings of the 22nd Engineering Applications of Neural Networks Conference PDF

Author: Lazaros Iliadis

Publisher: Springer Nature

Published: 2021-06-23

Total Pages: 521

ISBN-13: 3030805689

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This book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.

Recruitment Learning

Recruitment Learning PDF

Author: Joachim Diederich

Publisher: Springer Science & Business Media

Published: 2010-10-14

Total Pages: 316

ISBN-13: 3642140270

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This book presents a fascinating and self-contained account of "recruitment learning", a model and theory of fast learning in the neocortex. In contrast to the more common attractor network paradigm for long- and short-term memory, recruitment learning focuses on one-shot learning or "chunking" of arbitrary feature conjunctions that co-occur in single presentations. The book starts with a comprehensive review of the historic background of recruitment learning, putting special emphasis on the ground-breaking work of D.O. Hebb, W.A.Wickelgren, J.A.Feldman, L.G.Valiant, and L. Shastri. Afterwards a thorough mathematical analysis of the model is presented which shows that recruitment is indeed a plausible mechanism of memory formation in the neocortex. A third part extends the main concepts towards state-of-the-art spiking neuron models and dynamic synchronization as a tentative solution of the binding problem. The book further discusses the possible role of adult neurogenesis for recruitment. These recent developments put the theory of recruitment learning at the forefront of research on biologically inspired memory models and make the book an important and timely contribution to the field.

Theoretical Advances in Neural Computation and Learning

Theoretical Advances in Neural Computation and Learning PDF

Author: Vwani Roychowdhury

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 482

ISBN-13: 1461526965

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For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.

Neural Networks in Business Forecasting

Neural Networks in Business Forecasting PDF

Author: G. Peter Zhang

Publisher: IGI Global

Published: 2004-01-01

Total Pages: 311

ISBN-13: 1591401763

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Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Recurrent Neural Networks

Recurrent Neural Networks PDF

Author: Fathi M. Salem

Publisher: Springer Nature

Published: 2022-01-03

Total Pages: 130

ISBN-13: 3030899292

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This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.

Quantum Machine Learning

Quantum Machine Learning PDF

Author: Pethuru Raj

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2024-08-05

Total Pages: 336

ISBN-13: 3111342271

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Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.

The Pragmatic Turn

The Pragmatic Turn PDF

Author: Andreas K. Engel

Publisher: MIT Press

Published: 2016-06-03

Total Pages: 433

ISBN-13: 0262333309

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Experts from a range of disciplines assess the foundations and implications of a novel action-oriented view of cognition. Cognitive science is experiencing a pragmatic turn away from the traditional representation-centered framework toward a view that focuses on understanding cognition as “enactive.” This enactive view holds that cognition does not produce models of the world but rather subserves action as it is grounded in sensorimotor skills. In this volume, experts from cognitive science, neuroscience, psychology, robotics, and philosophy of mind assess the foundations and implications of a novel action-oriented view of cognition. Their contributions and supporting experimental evidence show that an enactive approach to cognitive science enables strong conceptual advances, and the chapters explore key concepts for this new model of cognition. The contributors discuss the implications of an enactive approach for cognitive development; action-oriented models of cognitive processing; action-oriented understandings of consciousness and experience; and the accompanying paradigm shifts in the fields of philosophy, brain science, robotics, and psychology. Contributors Moshe Bar, Lawrence W. Barsalov, Olaf Blanke, Jeannette Bohg, Martin V. Butz, Peter F. Dominey, Andreas K. Engel, Judith M. Ford, Karl J. Friston, Chris D. Frith, Shaun Gallagher, Antonia Hamilton, Tobias Heed, Cecilia Heyes, Elisabeth Hill, Matej Hoffmann, Jakob Hohwy, Bernhard Hommel, Atsushi Iriki, Pierre Jacob, Henrik Jörntell, Jürgen Jost, James Kilner, Günther Knoblich, Peter König, Danica Kragic, Miriam Kyselo, Alexander Maye, Marek McGann, Richard Menary, Thomas Metzinger, Ezequiel Morsella, Saskia Nagel, Kevin J. O'Regan, Pierre-Yves Oudeyer, Giovanni Pezzulo, Tony J. Prescott, Wolfgang Prinz, Friedemann Pulvermüller, Robert Rupert, Marti Sanchez-Fibla, Andrew Schwartz, Anil K. Seth, Vicky Southgate, Antonella Tramacere, John K. Tsotsos, Paul F. M. J. Verschure, Gabriella Vigliocco, Gottfried Vosgerau

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