| data science for neuroscience: Neural Data Science Erik Lee Nylen, Pascal Wallisch, 2017-02-24 A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. - Includes discussions of both MATLAB and Python in parallel - Introduces the canonical data analysis cascade, standardizing the data analysis flow - Presents tactics that strategically, tactically, and algorithmically help improve the organization of code |
| data science for neuroscience: Data-Driven Computational Neuroscience Concha Bielza, Pedro Larrañaga, 2020-11-26 Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data. |
| data science for neuroscience: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| data science for neuroscience: MATLAB for Neuroscientists Pascal Wallisch, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos, 2014-01-09 MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. - The first complete volume on MATLAB focusing on neuroscience and psychology applications - Problem-based approach with many examples from neuroscience and cognitive psychology using real data - Illustrated in full color throughout - Careful tutorial approach, by authors who are award-winning educators with strong teaching experience |
| data science for neuroscience: Guide to Research Techniques in Neuroscience Matt Carter, Rachel Essner, Nitsan Goldstein, Manasi Iyer, 2022-03-26 Modern neuroscience research is inherently multidisciplinary, with a wide variety of cutting edge new techniques to explore multiple levels of investigation. This Third Edition of Guide to Research Techniques in Neuroscience provides a comprehensive overview of classical and cutting edge methods including their utility, limitations, and how data are presented in the literature. This book can be used as an introduction to neuroscience techniques for anyone new to the field or as a reference for any neuroscientist while reading papers or attending talks. - Nearly 200 updated full-color illustrations to clearly convey the theory and practice of neuroscience methods - Expands on techniques from previous editions and covers many new techniques including in vivo calcium imaging, fiber photometry, RNA-Seq, brain spheroids, CRISPR-Cas9 genome editing, and more - Clear, straightforward explanations of each technique for anyone new to the field - A broad scope of methods, from noninvasive brain imaging in human subjects, to electrophysiology in animal models, to recombinant DNA technology in test tubes, to transfection of neurons in cell culture - Detailed recommendations on where to find protocols and other resources for specific techniques - Walk-through boxes that guide readers through experiments step-by-step |
| data science for neuroscience: An Introductory Course in Computational Neuroscience Paul Miller, 2018-10-09 A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits. |
| data science for neuroscience: Time Series Modeling of Neuroscience Data Tohru Ozaki, 2012-01-26 Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to |
| data science for neuroscience: Neuroscience Data in the Cloud National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2020-06-06 The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use. To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop. |
| data science for neuroscience: The Spike Mark Humphries, 2021-03-09 The story of a neural impulse and what it reveals about how our brains work We see the last cookie in the box and think, can I take that? We reach a hand out. In the 2.1 seconds that this impulse travels through our brain, billions of neurons communicate with one another, sending blips of voltage through our sensory and motor regions. Neuroscientists call these blips “spikes.” Spikes enable us to do everything: talk, eat, run, see, plan, and decide. In The Spike, Mark Humphries takes readers on the epic journey of a spike through a single, brief reaction. In vivid language, Humphries tells the story of what happens in our brain, what we know about spikes, and what we still have left to understand about them. Drawing on decades of research in neuroscience, Humphries explores how spikes are born, how they are transmitted, and how they lead us to action. He dives into previously unanswered mysteries: Why are most neurons silent? What causes neurons to fire spikes spontaneously, without input from other neurons or the outside world? Why do most spikes fail to reach any destination? Humphries presents a new vision of the brain, one where fundamental computations are carried out by spontaneous spikes that predict what will happen in the world, helping us to perceive, decide, and react quickly enough for our survival. Traversing neuroscience’s expansive terrain, The Spike follows a single electrical response to illuminate how our extraordinary brains work. |
| data science for neuroscience: 50 Years of Artificial Intelligence Max Lungarella, 2007-12-10 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. |
| data science for neuroscience: Challenges and Applications for Implementing Machine Learning in Computer Vision Kashyap, Ramgopal, Kumar, A.V. Senthil, 2019-10-04 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. |
| data science for neuroscience: Statistical Techniques for Neuroscientists Young K. Truong, Mechelle M. Lewis, 2016-10-04 Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful. |
| data science for neuroscience: From Neuroscience to Neurology Stephen Waxman, 2004-11-05 The field of neurology is being transformed, from a therapeutically nihilistic discipline with few effective treatments, to a therapeutic specialty which offers new, effective treatments for disorders of the brain and spinal cord. This remarkable transformation has bridged neuroscience, molecular medicine, and clinical investigation, and represents a major triumph for biomedical research. This book, which contains chapters by more than 29 internationally recognized authorities who have made major contributions to neurotherapeutics, tells the stories of how new treatments for disabling disorders of the nervous system, such as stroke, multiple sclerosis, Parkinson's disease, and migraine, were developed, and explores evolving themes and technologies that offer hope for even more effective treatments and ultimately cures for currently untreatable disorders of the brain and spinal cord. The first part of this book reviews the development of new therapies in neurology, from their inception in terms of basic science to their introduction into the clinical world. It also explores evolving themes and new technologies. This book will be of interest to everyone – clinicians and basic scientists alike – interested in diseases of the brain and spinal cord, and in the quest for new treatments for these disorders.* Presents the evolution of the field of neurology into a therapeutic discipline * Discusses lessons learned from past successes and applications to ongoing work* Explores the future of this field |
| data science for neuroscience: Statistical Signal Processing for Neuroscience and Neurotechnology Karim G. Oweiss, 2010-09-22 This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems |
| data science for neuroscience: Neuroscience and Multilingualism Edna Andrews, 2014-09-04 With a sharp focus, this culmination of cutting-edge research offers a new neuroscientific model for analysing multilingualism. Alongside a comprehensive analysis of the theoretical and experimental contributions to the field, it presents new data and analysis obtained from a multilingualism fMRI study. |
| data science for neuroscience: Neuronal Dynamics Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski, 2014-07-24 This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience. |
| data science for neuroscience: Analysis of Neural Data Robert E. Kass, Uri T. Eden, Emery N. Brown, 2014-07-08 Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work. |
| data science for neuroscience: Mathematical and Theoretical Neuroscience Giovanni Naldi, Thierry Nieus, 2018-03-20 This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical and numerical topics; statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience. |
| data science for neuroscience: Evolutionary Cognitive Neuroscience Steven Platek, Julian Keenan, Todd Kennedy Shackelford, 2007 An essential reference for the new discipline of evolutionary cognitive neuroscience that defines the field's approach of applying evolutionary theory to guide brain-behavior investigations. Since Darwin we have known that evolution has shaped all organisms and that biological organs—including the brain and the highly crafted animal nervous system—are subject to the pressures of natural and sexual selection. It is only relatively recently, however, that the cognitive neurosciences have begun to apply evolutionary theory and methods to the study of brain and behavior. This landmark reference documents and defines the emerging field of evolutionary cognitive neuroscience. Chapters by leading researchers demonstrate the power of the evolutionary perspective to yield new data, theory, and insights on the evolution and functional modularity of the brain. Evolutionary cognitive neuroscience covers all areas of cognitive neuroscience, from nonhuman brain-behavior relationships to human cognition and consciousness, and each section of Evolutionary Cognitive Neuroscience addresses a different adaptive problem. After an introductory section that outlines the basic tenets of both theory and methodology of an evolutionarily informed cognitive neuroscience, the book treats neuroanatomy from ontogenetic and phylogenetic perspectives and explores reproduction and kin recognition, spatial cognition and language, and self-awareness and social cognition. Notable findings include a theory to explain the extended ontogenetic and brain development periods of big-brained organisms, fMRI research on the neural correlates of romantic attraction, an evolutionary view of sex differences in spatial cognition, a theory of language evolution that draws on recent research on mirror neurons, and evidence for a rudimentary theory of mind in nonhuman primates. A final section discusses the ethical implications of evolutionary cognitive neuroscience and the future of the field. Contributors: C. Davison Ankney, Simon Baron-Cohen, S. Marc Breedlove, William Christiana, Michael Corballis, Robin I. M. Dunbar, Russell Fernald, Helen Fisher, Jonathan Flombaum, Farah Focquaert, Steven J.C. Gaulin, Aaron Goetz, Kevin Guise, Ruben C. Gur, William D. Hopkins, Farzin Irani, Julian Paul Keenan, Michael Kimberly, Stephen Kosslyn, Sarah L. Levin, Lori Marino, David Newlin, Ivan S. Panyavin, Shilpa Patel, Webb Phillips, Steven M. Platek, David Andrew Puts, Katie Rodak, J. Philippe Rushton, Laurie Santos, Todd K. Shackelford, Kyra Singh, Sean T. Stevens, Valerie Stone, Jaime W. Thomson, Gina Volshteyn, Paul Root Wolpe |
| data science for neuroscience: So You Want to Be a Neuroscientist? Ashley Juavinett, 2020-12-08 The pursuit to understand the human brain in all its intricacy is a fascinatingly complex challenge and neuroscience is one of the fastest-growing scientific fields worldwide. There is a wide range of career options open to those who wish to pursue a career in neuroscience, yet there are few resources that provide students with inside advice on how to go about it. So You Want to Be a Neuroscientist? is a contemporary and engaging guide for aspiring neuroscientists of diverse backgrounds and interests. Fresh with the experience of having recently launched her own career, Ashley Juavinett provides a candid look at the field, offering practical guidance that explores everything from programming to personal stories. Juavinett begins with a look at the field and its history, exploring our evolving understanding of how the brain works. She then tackles the nitty-gritty: how to apply to a PhD program, the daily life of a graduate student, the art of finding mentors and collaborators, and what to expect when working in a lab. Finally, she introduces readers to diverse young scientists whose career paths illustrate what you can do with a neuroscience degree. For anyone intrigued by the brain or seeking advice on how to further their ambitions of studying it, So You Want to Be a Neuroscientist? is a practical and timely overview of how to learn and thrive in this exciting field. |
| data science for neuroscience: Signal Processing for Neuroscientists Wim van Drongelen, 2006-12-18 Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670 |
| data science for neuroscience: Models of the Mind Grace Lindsay, 2021-03-04 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. |
| data science for neuroscience: Decision Neuroscience Jean-Claude Dreher, Léon Tremblay, 2016-09-27 Decision Neuroscience addresses fundamental questions about how the brain makes perceptual, value-based, and more complex decisions in non-social and social contexts. This book presents compelling neuroimaging, electrophysiological, lesional, and neurocomputational models in combination with hormonal and genetic approaches, which have led to a clearer understanding of the neural mechanisms behind how the brain makes decisions. The five parts of the book address distinct but inter-related topics and are designed to serve both as classroom introductions to major subareas in decision neuroscience and as advanced syntheses of all that has been accomplished in the last decade. Part I is devoted to anatomical, neurophysiological, pharmacological, and optogenetics animal studies on reinforcement-guided decision making, such as the representation of instructions, expectations, and outcomes; the updating of action values; and the evaluation process guiding choices between prospective rewards. Part II covers the topic of the neural representations of motivation, perceptual decision making, and value-based decision making in humans, combining neurcomputational models and brain imaging studies. Part III focuses on the rapidly developing field of social decision neuroscience, integrating recent mechanistic understanding of social decisions in both non-human primates and humans. Part IV covers clinical aspects involving disorders of decision making that link together basic research areas including systems, cognitive, and clinical neuroscience; this part examines dysfunctions of decision making in neurological and psychiatric disorders, such as Parkinson's disease, schizophrenia, behavioral addictions, and focal brain lesions. Part V focuses on the roles of various hormones (cortisol, oxytocin, ghrelin/leptine) and genes that underlie inter-individual differences observed with stress, food choices, and social decision-making processes. The volume is essential reading for anyone interested in decision making neuroscience. With contributions that are forward-looking assessments of the current and future issues faced by researchers, Decision Neuroscience is essential reading for anyone interested in decision-making neuroscience. - Provides comprehensive coverage of approaches to studying individual and social decision neuroscience, including primate neurophysiology, brain imaging in healthy humans and in various disorders, and genetic and hormonal influences on decision making - Covers multiple levels of analysis, from molecular mechanisms to neural-systems dynamics and computational models of how we make choices - Discusses clinical implications of process dysfunctions, including schizophrenia, Parkinson's disease, eating disorders, drug addiction, and pathological gambling - Features chapters from top international researchers in the field and full-color presentation throughout with numerous illustrations to highlight key concepts |
| data science for neuroscience: Neuroscience and Philosophy Felipe De Brigard, Walter Sinnott-Armstrong, 2022-02-01 Philosophers and neuroscientists address central issues in both fields, including morality, action, mental illness, consciousness, perception, and memory. Philosophers and neuroscientists grapple with the same profound questions involving consciousness, perception, behavior, and moral judgment, but only recently have the two disciplines begun to work together. This volume offers fourteen original chapters that address these issues, each written by a team that includes at least one philosopher and one neuroscientist who integrate disciplinary perspectives and reflect the latest research in both fields. Topics include morality, empathy, agency, the self, mental illness, neuroprediction, optogenetics, pain, vision, consciousness, memory, concepts, mind wandering, and the neural basis of psychological categories. The chapters first address basic issues about our social and moral lives: how we decide to act and ought to act toward each other, how we understand each other’s mental states and selves, and how we deal with pressing social problems regarding crime and mental or brain health. The following chapters consider basic issues about our mental lives: how we classify and recall what we experience, how we see and feel objects in the world, how we ponder plans and alternatives, and how our brains make us conscious and create specific mental states. |
| data science for neuroscience: The Idea of the Brain Matthew Cobb, 2020-04-21 An elegant, engrossing (Carol Tavris, Wall Street Journal) examination of what we think we know about the brain and why -- despite technological advances -- the workings of our most essential organ remain a mystery. I cannot recommend this book strongly enough.--Henry Marsh, author of Do No Harm For thousands of years, thinkers and scientists have tried to understand what the brain does. Yet, despite the astonishing discoveries of science, we still have only the vaguest idea of how the brain works. In The Idea of the Brain, scientist and historian Matthew Cobb traces how our conception of the brain has evolved over the centuries. Although it might seem to be a story of ever-increasing knowledge of biology, Cobb shows how our ideas about the brain have been shaped by each era's most significant technologies. Today we might think the brain is like a supercomputer. In the past, it has been compared to a telegraph, a telephone exchange, or some kind of hydraulic system. What will we think the brain is like tomorrow, when new technology arises? The result is an essential read for anyone interested in the complex processes that drive science and the forces that have shaped our marvelous brains. |
| data science for neuroscience: Do Zombies Dream of Undead Sheep? Timothy Verstynen, Bradley Voytek, 2016-10-04 A look at the true nature of the zombie brain Even if you've never seen a zombie movie or television show, you could identify an undead ghoul if you saw one. With their endless wandering, lumbering gait, insatiable hunger, antisocial behavior, and apparently memory-less existence, zombies are the walking nightmares of our deepest fears. What do these characteristic behaviors reveal about the inner workings of the zombie mind? Could we diagnose zombism as a neurological condition by studying their behavior? In Do Zombies Dream of Undead Sheep?, neuroscientists and zombie enthusiasts Timothy Verstynen and Bradley Voytek apply their neuro-know-how to dissect the puzzle of what has happened to the zombie brain to make the undead act differently than their human prey. Combining tongue-in-cheek analysis with modern neuroscientific principles, Verstynen and Voytek show how zombism can be understood in terms of current knowledge regarding how the brain works. In each chapter, the authors draw on zombie popular culture and identify a characteristic zombie behavior that can be explained using neuroanatomy, neurophysiology, and brain-behavior relationships. Through this exploration they shed light on fundamental neuroscientific questions such as: How does the brain function during sleeping and waking? What neural systems control movement? What is the nature of sensory perception? Walking an ingenious line between seriousness and satire, Do Zombies Dream of Undead Sheep? leverages the popularity of zombie culture in order to give readers a solid foundation in neuroscience. |
| data science for neuroscience: Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience Atli, Dincer, 2020-06-19 Marketing research in modern business has developed to include more than just data analytics. Today, an emerging interest within scientific marketing researches is the movement away from consumer research toward the use of direct neuroscientific approaches called neuromarketing. For companies to be profitable, they need to utilize the neuromarketing approach to understand how consumers view products and react to marketing, both consciously and unconsciously. Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience is a key reference source that provides relevant theoretical frameworks and the latest empirical research findings in the neuromarketing field. While highlighting topics such as advertising technologies, consumer behavior, and digital marketing, this publication explores cognitive practices and the methods of engaging customers on a neurological level. This book is ideally designed for marketers, advertisers, product developers, brand managers, consumer behavior analysts, consumer psychologists, managers, executives, behaviorists, business professionals, neuroscientists, academicians, and students. |
| data science for neuroscience: Neuroscience For Dummies Frank Amthor, 2016-04-14 Get on the fast track to understanding neuroscience Investigating how your senses work, how you move, and how you think and feel, Neuroscience For Dummies, 2nd Edition is your straight-forward guide to the most complicated structure known in the universe: the brain. Covering the most recent scientific discoveries and complemented with helpful diagrams and engaging anecdotes that help bring the information to life, this updated edition offers a compelling and plain-English look at how the brain and nervous system function. Simply put, the human brain is an endlessly fascinating subject: it holds the secrets to your personality, use of language, memories, and the way your body operates. In just the past few years alone, exciting new technologies and an explosion of knowledge have transformed the field of neuroscience—and this friendly guide is here to serve as your roadmap to the latest findings and research. Packed with new content on genetics and epigenetics and increased coverage of hippocampus and depression, this new edition of Neuroscience For Dummies is an eye-opening and fascinating read for readers of all walks of life. Covers how gender affects brain function Illustrates why some people are more sensitive to pain than others Explains what constitutes intelligence and its different levels Offers guidance on improving your learning What is the biological basis of consciousness? How are mental illnesses related to changes in brain function? Find the answers to these and countless other questions in Neuroscience For Dummies, 2nd Edition |
| data science for neuroscience: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science for neuroscience: Neuroscience Basics Jennifer L. Larimore, 2017-05-25 Neuroscience Basics: A Guide to the Brain’s Involvement in Everyday Activities examines how our brain works in everyday activities like sleeping, eating, love, and exercise. Many want to better understand how the brain works, but the terminology and jargon of books can be overwhelming. The book covers the basics taught in an introductory neurobiology course designed for anyone new to the neuroscience field, including non-neuroscientists. While each of the chapters explore the brain in a normal state, Neuroscience Basics also discusses disruptions of the normal state—psychosis, Alzheimer’s, Parkinson’s, autism, learning disorders, etc. This book breaks down the topics into language that is more accessible while making the neuroscience topics fun and relevant. Provides basic understanding of neuroscience topics that are part of everyday life Provides basic diagrams and descriptions of some basic anatomy Explores and explains current research in each of the chapters and topics Examines basics that are taught in an introductory neuroscience course to provide working knowledge of how the brain works for non-neuroscientists |
| data science for neuroscience: Neuroscience for Learning and Development Stella Collins, 2019-08-03 In order to design and deliver effective learning and development initiatives, it is essential to understand how our brains process and retain information. Neuroscience for Learning and Development introduces the latest research and concepts, equipping L&D and training professionals with an understanding of the inner workings of the mind. Covering areas such as how to create effective learning environments, promoting motivation and how to make learning 'stickier' through the use of stories, the book offers practical tools and ideas that can be applied in a variety of contexts, from digital learning and in-person training sessions, to coaching conversations, to lectures and presentations. Neuroscience for Learning and Development also features insights from L&D practitioners who have applied these approaches. Readers will not only find new techniques they can implement straight away, but will also discover research that backs up what they are already doing well, enabling them to put convincing cases to budget holders. This updated second edition contains new chapters on digital learning and on the importance of sleep, as well as updated wider content and new material on mindfulness, learning through your senses and the neuroscience of habits. |
| data science for neuroscience: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science for neuroscience: Early Childhood and Neuroscience Mine Conkbayir, 2017-01-12 Early Childhood and Neuroscience is a practical guide to understanding the complex and challenging subject of neuroscience and its use (and misapplication) in early childhood policy and practice. The author begins by introducing the definition and history of neuroscience. The reader is then led through structured chapters discussing questions such as: Why should practitioners know about neuroscience? How can neuroscience help practitioners better provide for babies and children? and Is it relevant? Topics covered include the nature vs. nurture debate through the lens of neuroscience, epigenetics, the first 1001 days and a discussion on just how critical the first three years of life are to healthy brain development. The book provides a balanced overview of the debates by weaving discussion on the opportunities of using neuroscience in early childhood practice with examination of the limitations and ethical implications throughout the chapters. This enables students to inform their own opinions about the discipline and its use in their future practice. Clear explanations of the main terms and theories are complemented with illustrative case studies of cutting-edge research from around the world, a glossary of key terms and suggestions for further reading. Reflective discussion questions give students the chance to apply their theoretical knowledge to real-world contexts. These features encourage and support independent critical thinking, helping students to reflect on, evaluate and analyse a range of ideas, research findings and applications for their own future early childhood practice. Early Childhood and Neuroscience is essential reading for lecturers, undergraduate and postgraduate students in the field as well as for the new practitioner. |
| data science for neuroscience: Mapping the Brain and Its Functions Institute of Medicine, Division of Biobehavioral Sciences and Mental Disorders, Division of Health Sciences Policy, Committee on a National Neural Circuitry Database, 1991-02-01 Significant advances in brain research have been made, but investigators who face the resulting explosion of data need new methods to integrate the pieces of the brain puzzle. Based on the expertise of more than 100 neuroscientists and computer specialists, this new volume examines how computer technology can meet that need. Featuring outstanding color photography, the book presents an overview of the complexity of brain research, which covers the spectrum from human behavior to genetic mechanisms. Advances in vision, substance abuse, pain, and schizophrenia are highlighted. The committee explores the potential benefits of computer graphics, database systems, and communications networks in neuroscience and reviews the available technology. Recommendations center on a proposed Brain Mapping Initiative, with an agenda for implementation and a look at issues such as privacy and accessibility. |
| data science for neuroscience: Fundamentals of Computational Neuroscience Thomas Trappenberg, 2010 The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. |
| data science for neuroscience: Principles of Brain Dynamics Mikhail I. Rabinovich, Karl J. Friston, Pablo Varona, 2023-12-05 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 science for neuroscience: Python in Neuroscience Eilif Muller, James A. Bednar, Markus Diesmann, Marc-Oliver Gewaltig, Michael Hines, Andrew P. Davison, 2015-07-23 Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development. |
| data science for neuroscience: Consumer Neuroscience Moran Cerf, Manuel Garcia-Garcia, 2017-11-16 A comprehensive introduction to using the tools and techniques of neuroscience to understand how consumers make decisions about purchasing goods and services. Contrary to the assumptions of economists, consumers are not always rational actors who make decisions in their own best interests. The new field of behavioral economics draws on the insights of psychology to study non-rational decision making. The newer field of consumer neuroscience draws on the findings, tools, and techniques of neuroscience to understand how consumers make judgments and decisions. This book is the first comprehensive treatment of consumer neuroscience, suitable for classroom use or as a reference for business and marketing practitioners. After an overview of the field, the text offers the background on the brain and physiological systems necessary for understanding how they work in the context of decision making and reviews the sensory and perceptual mechanisms that govern our perception and experience. Chapters by experts in the field investigate tools for studying the brain, including fMRI, EEG, eye-tracking, and biometrics, and their possible use in marketing. The book examines the relation of attention, memory, and emotion to consumer behavior; cognitive factors in decision making; and the brain's reward system. It describes how consumers develop implicit associations with a brand, perceptions of pricing, and how consumer neuroscience can encourage healthy behaviors. Finally, the book considers ethical issues raised by the application of neuroscience tools to marketing. Contributors Fabio Babiloni, Davide Baldo, David Brandt, Moran Cerf, Yuping Chen, Patrizia Cherubino, Kimberly Rose Clark, Maria Cordero-Merecuana, William A. Cunningham, Manuel Garcia-Garcia, Ming Hsu, Ana Iorga, Philip Kotler, Carl Marci, Hans Melo, Kai-Markus Müller, Brendan Murray, Ingrid L. C. Nieuwenhuis, Graham Page, Hirak Parikh, Dante M. Pirouz, Martin Reimann, Neal J. Roese, Irit Shapira-Lichter, Daniela Somarriba, Julia Trabulsi, Arianna Trettel, Giovanni Vecchiato, Thalia Vrantsidis, Sarah Walker |
| data science for neuroscience: Cortex and Mind Joaquin M. Fuster, 2003 This book presents a unique synthesis of the current neuroscience of cognition by one of the world's authorities in the field. The guiding principle to this synthesis is the tenet that the entirety of our knowledge is encoded by relations, and thus by connections, in neuronal networks of our cerebral cortex. Cognitive networks develop by experience on a base of widely dispersed modular cell assemblies representing elementary sensations and movements. As they develop cognitive networks organize themselves hierarchically by order of complexity or abstraction of their content. Because networks intersect profusely, sharing commong nodes, a neuronal assembly anywhere in the cortex can be part of many networks, and therefore many items of knowledge. All cognitive functions consist of neural transactions within and between cognitive networks. After reviewing the neurobiology and architecture of cortical networks (also named cognits), the author undertakes a systematic study of cortical dynamics in each of the major cognitive functions--perception, memory, attention, language, and intelligence. In this study, he makes use of a large body of evidence from a variety of methodologies, in the brain of the human as well as the nonhuman primate. The outcome of his interdisciplinary endeavor is the emergence of a structural and dynamic order in the cerebral cortex that, though still sketchy and fragmentary, mirrors with remarkable fidelity the order in the human mind. |
| data science for neuroscience: Signal Processing in Neuroscience Xiaoli Li, 2016-08-31 This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensors such as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
