Machine Learning For Neuroscience

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Machine Learning in Clinical Neuroscience

Author : Victor E. Staartjes
Publisher : Springer Nature
ISBN 13 : 303085292X
Total Pages : 343 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Machine Learning in Clinical Neuroscience by : Victor E. Staartjes

Download or read book Machine Learning in Clinical Neuroscience written by Victor E. Staartjes and published by Springer Nature. This book was released on 2021-12-03 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.

Principles of Brain Dynamics

Author : Mikhail I. Rabinovich
Publisher : MIT Press
ISBN 13 : 0262549905
Total Pages : 371 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Principles of Brain Dynamics by : Mikhail I. Rabinovich

Download or read book Principles of Brain Dynamics written by Mikhail I. Rabinovich and published by MIT Press. This book was released on 2023-12-05 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field. The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field. It includes contributions from both theoreticians and experimentalists, offering an eclectic treatment of fundamental issues. Topics addressed range from experimental and computational approaches to transient brain dynamics to the free-energy principle as a global brain theory. The book concludes with a short but rigorous guide to modern nonlinear dynamics and their application to neural dynamics.

Data-Driven Computational Neuroscience

Author : Concha Bielza
Publisher : Cambridge University Press
ISBN 13 : 110849370X
Total Pages : 709 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Data-Driven Computational Neuroscience by : Concha Bielza

Download or read book Data-Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

50 Years of Artificial Intelligence

Author : Max Lungarella
Publisher : Springer Science & Business Media
ISBN 13 : 3540772952
Total Pages : 408 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis 50 Years of Artificial Intelligence by : Max Lungarella

Download or read book 50 Years of Artificial Intelligence written by Max Lungarella and published by Springer Science & Business Media. This book was released on 2007-12-10 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift volume, published in celebration of the 50th Anniversary of Artificial Intelligence, includes 34 refereed papers written by leading researchers in the field of Artificial Intelligence. The papers were carefully selected from the invited lectures given at the 50th Anniversary Summit of AI, held at the Centro Stefano Franscini, Monte Verità, Ascona, Switzerland, July 9-14, 2006. The summit provided a venue for discussions on a broad range of topics.

Challenges and Applications for Implementing Machine Learning in Computer Vision

Author : Kashyap, Ramgopal
Publisher : IGI Global
ISBN 13 : 1799801845
Total Pages : 293 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Challenges and Applications for Implementing Machine Learning in Computer Vision by : Kashyap, Ramgopal

Download or read book Challenges and Applications for Implementing Machine Learning in Computer Vision written by Kashyap, Ramgopal and published by IGI Global. This book was released on 2019-10-04 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications

Author : Alonso, Eduardo
Publisher : IGI Global
ISBN 13 : 1609600231
Total Pages : 396 pages
Book Rating : 4.6/5 (96 download)

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Book Synopsis Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications by : Alonso, Eduardo

Download or read book Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications written by Alonso, Eduardo and published by IGI Global. This book was released on 2010-11-30 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

Machine Learning

Author : Andrea Mechelli
Publisher : Academic Press
ISBN 13 : 0128157402
Total Pages : 412 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Machine Learning by : Andrea Mechelli

Download or read book Machine Learning written by Andrea Mechelli and published by Academic Press. This book was released on 2019-11-14 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Connectome

Author : Sebastian Seung
Publisher : Houghton Mifflin Harcourt
ISBN 13 : 0547508182
Total Pages : 389 pages
Book Rating : 4.5/5 (475 download)

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Book Synopsis Connectome by : Sebastian Seung

Download or read book Connectome written by Sebastian Seung and published by Houghton Mifflin Harcourt. This book was released on 2012 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: The audacious effort to map the brain--and along with it mental afflictions, from autism to schizophrenia--by a rising star in neuroscience.

Unsupervised Learning

Author : Geoffrey Hinton
Publisher : MIT Press
ISBN 13 : 9780262581684
Total Pages : 420 pages
Book Rating : 4.5/5 (816 download)

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Book Synopsis Unsupervised Learning by : Geoffrey Hinton

Download or read book Unsupervised Learning written by Geoffrey Hinton and published by MIT Press. This book was released on 1999-05-24 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

Models of the Mind

Author : Grace Lindsay
Publisher : Bloomsbury Publishing
ISBN 13 : 1472966457
Total Pages : 401 pages
Book Rating : 4.4/5 (729 download)

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Book Synopsis Models of the Mind by : Grace Lindsay

Download or read book Models of the Mind written by Grace Lindsay and published by Bloomsbury Publishing. This book was released on 2021-03-04 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human brain is made up of 85 billion neurons, which are connected by over 100 trillion synapses. For more than a century, a diverse array of researchers searched for a language that could be used to capture the essence of what these neurons do and how they communicate – and how those communications create thoughts, perceptions and actions. The language they were looking for was mathematics, and we would not be able to understand the brain as we do today without it. In Models of the Mind, author and computational neuroscientist Grace Lindsay explains how mathematical models have allowed scientists to understand and describe many of the brain's processes, including decision-making, sensory processing, quantifying memory, and more. She introduces readers to the most important concepts in modern neuroscience, and highlights the tensions that arise when the abstract world of mathematical modelling collides with the messy details of biology. Each chapter of Models of the Mind focuses on mathematical tools that have been applied in a particular area of neuroscience, progressing from the simplest building block of the brain – the individual neuron – through to circuits of interacting neurons, whole brain areas and even the behaviours that brains command. In addition, Grace examines the history of the field, starting with experiments done on frog legs in the late eighteenth century and building to the large models of artificial neural networks that form the basis of modern artificial intelligence. Throughout, she reveals the value of using the elegant language of mathematics to describe the machinery of neuroscience.

Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning

Author : Lei Deng
Publisher : Frontiers Media SA
ISBN 13 : 2889667421
Total Pages : 200 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning by : Lei Deng

Download or read book Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning written by Lei Deng and published by Frontiers Media SA. This book was released on 2021-05-05 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

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Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Signal Processing and Machine Learning for Brain-Machine Interfaces

Author : Toshihisa Tanaka
Publisher : Institution of Engineering and Technology
ISBN 13 : 1785613987
Total Pages : 355 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Signal Processing and Machine Learning for Brain-Machine Interfaces by : Toshihisa Tanaka

Download or read book Signal Processing and Machine Learning for Brain-Machine Interfaces written by Toshihisa Tanaka and published by Institution of Engineering and Technology. This book was released on 2018-09 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces signal processing and machine learning techniques for Brain Machine Interfacing/Brain Computer Interfacing (BMI/BCI), and their practical and future applications in neuroscience, medicine, and rehabilitation. This is an emerging and challenging technology in engineering, computing, machine learning, neuroscience and medicine, and so the book will interest researchers, engineers, professionals and specialists from all of these areas who need to know more about cutting edge technologies in the fields.

Reinforcement Learning, second edition

Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton

Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Deep Learning in Science

Author : Pierre Baldi
Publisher : Cambridge University Press
ISBN 13 : 1108845355
Total Pages : 387 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Deep Learning in Science by : Pierre Baldi

Download or read book Deep Learning in Science written by Pierre Baldi and published by Cambridge University Press. This book was released on 2021-07 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.

The Deep Learning Revolution

Author : Terrence J. Sejnowski
Publisher : MIT Press
ISBN 13 : 026203803X
Total Pages : 354 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis The Deep Learning Revolution by : Terrence J. Sejnowski

Download or read book The Deep Learning Revolution written by Terrence J. Sejnowski and published by MIT Press. This book was released on 2018-10-23 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.