Machine Learning In Neuroscience

Author : Victor E. Staartjes,Luca Regli,Carlo Serra
Publisher : Springer Nature
Page : 343 pages
File Size : 52,7 Mb
Release : 2021-12-03
Category : Medical
ISBN : 9783030852924

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Machine Learning in Clinical Neuroscience by Victor E. Staartjes,Luca Regli,Carlo Serra Pdf

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.

Author : Concha Bielza,Pedro Larrañaga
Publisher : Cambridge University Press
Page : 709 pages
File Size : 52,9 Mb
Release : 2020-11-26
Category : Computers
ISBN : 9781108493703

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Data-Driven Computational Neuroscience by Concha Bielza,Pedro Larrañaga Pdf

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

Author : Mikhail I. Rabinovich,Karl J. Friston,Pablo Varona
Publisher : MIT Press
Page : 371 pages
File Size : 48,6 Mb
Release : 2023-12-05
Category : Medical
ISBN : 9780262549905

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Principles of Brain Dynamics by Mikhail I. Rabinovich,Karl J. Friston,Pablo Varona Pdf

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.

Author : Hamid R. Rabiee,Ali Ghazizadeh,Mackenzie W. Mathis,Reza Lashgari
Publisher : Frontiers Media SA
Page : 361 pages
File Size : 50,8 Mb
Release : 2023-01-27
Category : Science
ISBN : 9782832510292

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Machine learning in neuroscience by Hamid R. Rabiee,Ali Ghazizadeh,Mackenzie W. Mathis,Reza Lashgari Pdf

Author : Max Lungarella
Publisher : Springer Science & Business Media
Page : 408 pages
File Size : 52,7 Mb
Release : 2007-12-10
Category : Computers
ISBN : 9783540772958

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50 Years of Artificial Intelligence by Max Lungarella Pdf

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.

Author : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 47,7 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404

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Machine Learning by Andrea Mechelli,Sandra Vieira Pdf

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

Author : Kashyap, Ramgopal,Kumar, A.V. Senthil
Publisher : IGI Global
Page : 293 pages
File Size : 55,9 Mb
Release : 2019-10-04
Category : Computers
ISBN : 9781799801849

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Challenges and Applications for Implementing Machine Learning in Computer Vision by Kashyap, Ramgopal,Kumar, A.V. Senthil Pdf

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.

Author : Grace Lindsay
Publisher : Bloomsbury Publishing
Page : 401 pages
File Size : 42,7 Mb
Release : 2021-03-04
Category : Science
ISBN : 9781472966452

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Models of the Mind by Grace Lindsay Pdf

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.

Author : Lei Deng,Kaushik Roy,Huajin Tang
Publisher : Frontiers Media SA
Page : 200 pages
File Size : 49,7 Mb
Release : 2021-05-05
Category : Science
ISBN : 9782889667420

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Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning by Lei Deng,Kaushik Roy,Huajin Tang Pdf

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 48,8 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262352703

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Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

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.

Author : Terrence J. Sejnowski
Publisher : MIT Press
Page : 354 pages
File Size : 49,6 Mb
Release : 2018-10-23
Category : Computers
ISBN : 9780262038034

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The Deep Learning Revolution by Terrence J. Sejnowski Pdf

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.

Author : Geoffrey Hinton,Terrence J. Sejnowski
Publisher : MIT Press
Page : 420 pages
File Size : 44,7 Mb
Release : 1999-05-24
Category : Medical
ISBN : 026258168X

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Unsupervised Learning by Geoffrey Hinton,Terrence J. Sejnowski Pdf

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.

Author : Jeff Hawkins
Publisher : Basic Books
Page : 251 pages
File Size : 54,5 Mb
Release : 2021-03-02
Category : Computers
ISBN : 9781541675803

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A Thousand Brains by Jeff Hawkins Pdf

A bestselling author, neuroscientist, and computer engineer unveils a theory of intelligence that will revolutionize our understanding of the brain and the future of AI. For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world—not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought. A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word. One of the Financial Times' Best Books of 2021 One of Bill Gates' Five Favorite Books of 2021

Author : Marcel van Gerven,Sander Bohte
Publisher : Frontiers Media SA
Page : 220 pages
File Size : 44,9 Mb
Release : 2018-02-01
Category : Electronic
ISBN : 9782889454013

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Artificial Neural Networks as Models of Neural Information Processing by Marcel van Gerven,Sander Bohte Pdf

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.

Author : Toshihisa Tanaka,Mahnaz Arvaneh
Publisher : Institution of Engineering and Technology
Page : 355 pages
File Size : 51,7 Mb
Release : 2018-09
Category : Technology & Engineering
ISBN : 9781785613982

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Signal Processing and Machine Learning for Brain-Machine Interfaces by Toshihisa Tanaka,Mahnaz Arvaneh Pdf

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.