New Frontiers in Brain
New Frontiers in Brain

Author: Nawaz Mohamudally

Publisher: BoD – Books on Demand

Published: 2020-02-26

Total Pages: 144

ISBN-13: 1838804994

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Brain-Computer Interface (BCI) sounds comparable to plugging a USB cable into a human brain with a laptop and accessing brain information. However, it is not as simple as it sounds. BCI is a multidisciplinary discipline with an exponential progress parallel to and with Artificial Intelligence for the past decades. Initially started with the Electroencephalography (EEG) analysis, BCI offers practical applications for cortical physiology today. Although BCI outcomes are more perceptible in medicine such as cognitive assessment, neurofeedback, and neuroprosthetic implants, it opens up amazing avenues for the business community through machine learning and robotics. Thought-to-text is one example of a hot topic in BCI. So, it is quite predictable to see BCI for individual usage given the current affordability of platforms for less technologically savvy users as well as BCI integrated within office automation productivity tools. The current trend is towards vulgarization for businesses benefits, by extension to the society at large. Thus, the interest in preparing a book on BCI. This book aims to compile and disseminate the latest research findings and best practices on how BCI is expanding the frontiers of knowledge in clinical practices, on the brain itself, and the underlying technologies.

Neuronal Dynamics
Neuronal Dynamics

Author: Wulfram Gerstner

Publisher: Cambridge University Press

Published: 2014-07-24

Total Pages: 591

ISBN-13: 1107060834

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This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

Neural Networks and Statistical Learning
Neural Networks and Statistical Learning

Author: Ke-Lin Du

Publisher: Springer Science & Business Media

Published: 2013-12-09

Total Pages: 834

ISBN-13: 1447155718

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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Spiking Neuron Models
Spiking Neuron Models

Author: Wulfram Gerstner

Publisher: Cambridge University Press

Published: 2002-08-15

Total Pages: 498

ISBN-13: 9780521890793

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Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Author: Nikola K. Kasabov

Publisher: Springer

Published: 2018-08-29

Total Pages: 742

ISBN-13: 3662577151

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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.

Understanding the Role of Time-Dimension in the Brain Information Processing
Understanding the Role of Time-Dimension in the Brain Information Processing

Author: Daya Shankar Gupta

Publisher: Frontiers Media SA

Published: 2017-04-13

Total Pages: 139

ISBN-13: 2889451496

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Optimized interaction of the brain with environment requires the four-dimensional representation of space-time in the neuronal circuits. Information processing is an important part of this interaction, which is critically dependent on time-dimension. Information processing has played an important role in the evolution of mammals, and has reached a level of critical importance in the lives of primates, particularly the humans. The entanglement of time-dimension with information processing in the brain is not clearly understood at present. Time-dimension in physical world – the environment of an organism – can be represented by the interval of a pendulum swing (the cover page depicts temporal unit with the help of a swinging pendulum). Temporal units in neural processes are represented by regular activities of pacemaker neurons, tonic regular activities of proprioceptors and periodic fluctuations in the excitability of neurons underlying brain oscillations. Moreover, temporal units may be representationally associated with time-bins containing bits of information (see the Editorial), which may be studied to understand the entanglement of time-dimension with neural information processing. The optimized interaction of the brain with environment requires the calibration of neural temporal units. Neural temporal units are calibrated as a result of feedback processes occurring during the interaction of an organism with environment. Understanding the role of time-dimension in the brain information processing requires a multidisciplinary approach, which would include psychophysics, single cell studies and brain recordings. Although this Special Issue has helped us move forward on some fronts, including theoretical understanding of calibration of time-information in neural circuits, and the role of brain oscillations in timing functions and integration of asynchronous sensory information, further advancements are needed by developing correct computational tools to resolve the relationship between dynamic, hierarchical neural oscillatory structures that form during the brain’s interaction with environment.

The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks

Author: Michael A. Arbib

Publisher: MIT Press

Published: 2003

Total Pages: 1328

ISBN-13: 0262011972

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This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

Pulsed Neural Networks
Pulsed Neural Networks

Author: Wolfgang Maass

Publisher: MIT Press

Published: 2001-01-26

Total Pages: 414

ISBN-13: 9780262632218

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Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador