Advertisement
| analysis of neural data: 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. |
| analysis of neural data: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-10-28 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. |
| analysis of neural data: Analyzing Neural Time Series Data Mike X Cohen, 2014-01-17 A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches. |
| analysis of neural data: 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 |
| analysis of neural data: Advanced Data Analysis in Neuroscience Daniel Durstewitz, 2017-09-15 This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanatory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function. Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck |
| analysis of neural data: 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. |
| analysis of neural data: 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. |
| analysis of neural data: Fundamentals of Brain Network Analysis Alex Fornito, Andrew Zalesky, Edward Bullmore, 2016-03-04 Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain |
| analysis of neural data: 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 |
| analysis of neural data: Spikes Fred Rieke, 1997 Intended for neurobiologists with an interest in mathematical analysis of neural data as well as the growing number of physicists and mathematicians interested in information processing by real nervous systems, Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory. |
| analysis of neural data: 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 |
| analysis of neural data: Advanced State Space Methods for Neural and Clinical Data Zhe Chen, 2015-10-15 An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data. |
| analysis of neural data: Sensitivity Analysis for Neural Networks Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng, 2009-11-09 Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks. |
| analysis of neural data: Bayesian Data Analysis for the Behavioral and Neural Sciences Todd E. Hudson, 2021-06-30 This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond frequentist concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called hypothesis testing) problems most frequently encountered in real-world applications. |
| analysis of neural data: Stability Analysis of Neural Networks Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam, 2021-12-05 This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists. |
| analysis of neural data: Mathematics for Neuroscientists Fabrizio Gabbiani, Steven James Cox, 2017-02-04 Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. - Fully revised material and corrected text - Additional chapters on extracellular potentials, motion detection and neurovascular coupling - Revised selection of exercises with solutions - More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts |
| analysis of neural data: Advances in Neural Signal Processing Ramana Vinjamuri, 2020-09-09 Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications. |
| analysis of neural data: Decision Making, Affect, and Learning Mauricio R. Delgado, Elizabeth A. Phelps, Trevor W. Robbins, 2011-03-24 Focuses on decision making and emotional processing, investigating the psychological and neural systems underlying decision making, and the relationship with reward, affect, and learning. Considers neurodevelopmental and clinical aspects and looks at the applied aspects for other disciplines, including neuroeconomics. |
| analysis of neural data: Theoretical Neuroscience Peter Dayan, Laurence F. Abbott, 2005-08-12 Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site. |
| analysis of neural data: Neural Networks and Statistical Learning Ke-Lin Du, M. N. S. Swamy, 2013-12-09 Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining. |
| analysis of neural data: An Introduction to Modeling Neuronal Dynamics Christoph Börgers, 2017-04-17 This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book. |
| analysis of neural data: 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. |
| analysis of neural data: Mathematical Methods for Neural Network Analysis and Design Richard M. Golden, 1996 For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion. |
| analysis of neural data: Unsupervised Learning Geoffrey Hinton, Terrence J. Sejnowski, 1999-05-24 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. |
| analysis of neural data: Introduction To The Theory Of Neural Computation John A. Hertz, 2018-03-08 Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest. |
| analysis of neural data: Bayesian Brain Kenji Doya, Shin Ishii, Alexandre Pouget, 2007 Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control. |
| analysis of neural data: Handbook of Functional MRI Data Analysis Russell A. Poldrack, Jeanette A. Mumford, Thomas E. Nichols, 2024-02-08 Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook for Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM, and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling, and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software. |
| analysis of neural data: Convergence Analysis of Recurrent Neural Networks Zhang Yi, 2013-11-11 Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs. |
| analysis of neural data: Dynamic Neuroscience Zhe Chen, Sridevi V. Sarma, 2017-12-27 This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers. |
| analysis of neural data: Observed Brain Dynamics Partha Mitra, 2007-12-07 The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area. The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It may also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing. The first part of the book contains a set of essays meant to provide conceptual background which are not technical and shall be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics. |
| analysis of neural data: Neural Engineering Chris Eliasmith, Charles H. Anderson, 2003 A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems. |
| analysis of neural data: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| analysis of neural data: Handbook of Neural Activity Measurement Romain Brette, Alain Destexhe, 2012-09-06 Underlying principles of the various techniques are explained, enabling neuroscientists to extract meaningful information from their measurements. |
| analysis of neural data: Methods for Neural Ensemble Recordings Miguel A. L. Nicolelis, 2007-12-03 Extensively updated and expanded, this second edition of a bestseller distills the current state-of-the-science and provides the nuts and bolts foundation of the methods involved in this rapidly growing science. With contributions from pioneering researchers, it includes microwire array design for chronic neural recordings, new surgical techniques for chronic implantation, microelectrode microstimulation of brain tissue, multielectrode recordings in the somatosensory system and during learning, as well as recordings from the central gustatory-reward pathways. It explores the use of Brain-Machine Interface to restore neurological function and proposes conceptual and technical approaches to human neural ensemble recordings in the future. |
| analysis of neural data: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| analysis of neural data: Topological Data Analysis Nils A. Baas, Gunnar E. Carlsson, Gereon Quick, Markus Szymik, Marius Thaule, 2020-06-25 This book gathers the proceedings of the 2018 Abel Symposium, which was held in Geiranger, Norway, on June 4-8, 2018. The symposium offered an overview of the emerging field of Topological Data Analysis. This volume presents papers on various research directions, notably including applications in neuroscience, materials science, cancer biology, and immune response. Providing an essential snapshot of the status quo, it represents a valuable asset for practitioners and those considering entering the field. |
| analysis of neural data: Neuroscience Databases Rolf Kötter, 2003 Neuroscience Databases: A Practical Guide is the first book providing a comprehensive overview of these increasingly important databases. This volume makes the results of the Human Genome Project and other recent large-scale initiatives in the neurosciences available to a wider community. It extends the scope of bioinformatics from the molecular to the cellular, microcircuitry and systems levels, dealing for the first time with complex neuroscientific issues and leading the way to a new culture of data sharing and data mining necessary to successfully tackle neuroscience questions. Aimed at the novice user who wants to access the data, it provides clear and concise instructions on how to download the available data sets and how to use the software with a minimum of technical detail with most chapters written by the database creators themselves. Key databases and topics include: -Neuroinformatics for C. Elegans; -Gene Expression Patterns; -Functional Analyses of Olfactory Receptors -Protein-Protein Interactions; -Web-Based Neuronal Archives; -Neuronal and Network Modeling; -Storage and Retrieval of Experimental Data for Biophysically Realistic Modeling; -Analysis of Spike Trains; -Neural Connectivity Patterns; -Software Tools for Neuroimaging; -Data Management, Inspection and Sharing. |
| analysis of neural data: Spiking Neuron Models Wulfram Gerstner, Werner M. Kistler, 2002-08-15 Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed. |
| analysis of neural data: 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 |
| analysis of neural data: 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. |
Statistical analysis of neural data - sites.stat.columbia.edu
estimate the model parameters here? We begin by writing down the likelihood p(Dj ; ~x) of the observed spike data D given the model parameter and the observed stimulus ~x, and then we …
Computational Neuroscience: Mathematical and Statistical …
In this article, we focus on a fundamental component of computational neuroscience, the modeling of neural activity recorded in the form of action potentials (APs), known as spikes, …
Neural data science: accelerating the experiment-analysis …
Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control — developed in lockstep with advances in experimental neurotechnology — …
Analyzing neural time series data - Mike X Cohen
5.1 Biophysical events that are measurable with EEG 5.2 Neurobiological mechanisms of oscillations 5.3 Phase‐locked, time‐locked, task‐related 5.4 Neurophysiological mechanisms of …
Statistical modeling and analysis of neural data (NEU 560)
Write down the form of the ELBO that we typically write for the M-step (feel free to leave off any additive constants). (Bonus: write it as an expectation.) Consider a mixture-of-Gaussians with …
Statistical analysis of neural data (GR8201) - Department of …
Course goals: We will introduce a number of advanced statistical techniques relevant in neuroscience. Each technique will be illustrated via application to problems in neuroscience.
Robert E. Kass Uri T. Eden Emery N. Brown Analysis of Neural …
Some readers may expect a book organized by type of neural data. We decided, instead, to organize by analysis, with each chapter devoted to broadly categorized statistical concepts …
Biology 4/510: Analysis of Neural Data
Small groups of students will perform a novel analysis on a publicly available dataset, write a short report (3-5 pages) on the results, and prepare a 10-minute presentation on their findings to …
TUTORIAL: BRAIN CONNECTOME ANALYSIS WITH GRAPH …
By pro-viding a comprehensive overview of brain network analysis with Graph Neural Networks (GNNs), it can help researchers, practitioners, and students understand the latest deep …
Using population decoding to analyze neural data
To study this, neuroscientists make different types of neural recordings, including functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings from …
Statistical analysis of neural data - sites.stat.columbia.edu
neural examples. To properly introduce the basic ideas behind the EM method, we will need to develop just a bit of background on an optimization technique known as “bound optimization.” …
PSYC696B: Analyzing Neural Time-series Data - University of …
Oscillations are a fundamental neural mechanism that supports aspects of synaptic, cellular, and systems-level brain function across multiple spatial and temporal scales (Cohen, 2014)
Statistical modeling and analysis of neural data (NEU 560)
(aka “complete-data log-likelihood”) Two useful forms: expected total-data log-li entropy of q(z) Statistical Modeling and Analysis of Neural Data (NEU 560) Princeton University, Spring 2018 …
Emerging techniques in statistical analysis of neural data
Availability of these new data has in turn led to the emergence of a new field of computational and data-intensive neuroscience.
Statistical analysis of neural data - sites.stat.columbia.edu
HMMs have widespread applications in time-series analysis, notably in speech processing, bioinformatics, and control theory, and we will describe a wide variety of applications in …
Statistical modeling and analysis of neural data (NEU 560)
Statistical modeling and analysis of neural data (NEU 560) Jonathan Pillow Fall 2020 1
Statistical analysis of neural data - sites.stat.columbia.edu
Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves. Biological Cybernetics, 99:361–370. Badel, L., Lefort, S., Brette, R., Petersen, C. C. H., …
Statistical analysis of neural data - sites.stat.columbia.edu
The neural decoding problem is a fundamental question in computational neuroscience (Rieke et al., 1997): given the observed spike trains of a population of cells whose responses are related …
Statistical Modeling and Analysis of Neural Data (NEU 560)
This course aims to introduce students to advanced statistical and machine learning methods for analyzing of neural data, with an emphasis on methods derived from regression (supervised) …
Statistical analysis of neural data - sites.stat.columbia.edu
estimate the model parameters here? We begin by writing down the likelihood p(Dj ; ~x) of the observed spike data D given the model parameter and the observed stimulus ~x, and then we …
Computational Neuroscience: Mathematical and …
In this article, we focus on a fundamental component of computational neuroscience, the modeling of neural activity recorded in the form of action potentials (APs), known as spikes, …
Neural data science: accelerating the experiment-analysis …
Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control — developed in lockstep with advances in experimental neurotechnology — …
Analyzing neural time series data - Mike X Cohen
5.1 Biophysical events that are measurable with EEG 5.2 Neurobiological mechanisms of oscillations 5.3 Phase‐locked, time‐locked, task‐related 5.4 Neurophysiological mechanisms of …
Statistical modeling and analysis of neural data (NEU 560)
Write down the form of the ELBO that we typically write for the M-step (feel free to leave off any additive constants). (Bonus: write it as an expectation.) Consider a mixture-of-Gaussians with …
Statistical analysis of neural data (GR8201) - Department of …
Course goals: We will introduce a number of advanced statistical techniques relevant in neuroscience. Each technique will be illustrated via application to problems in neuroscience.
Robert E. Kass Uri T. Eden Emery N. Brown Analysis of Neural …
Some readers may expect a book organized by type of neural data. We decided, instead, to organize by analysis, with each chapter devoted to broadly categorized statistical concepts …
Biology 4/510: Analysis of Neural Data
Small groups of students will perform a novel analysis on a publicly available dataset, write a short report (3-5 pages) on the results, and prepare a 10-minute presentation on their findings to …
TUTORIAL: BRAIN CONNECTOME ANALYSIS WITH GRAPH …
By pro-viding a comprehensive overview of brain network analysis with Graph Neural Networks (GNNs), it can help researchers, practitioners, and students understand the latest deep …
Using population decoding to analyze neural data
To study this, neuroscientists make different types of neural recordings, including functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings from …
Statistical analysis of neural data - sites.stat.columbia.edu
neural examples. To properly introduce the basic ideas behind the EM method, we will need to develop just a bit of background on an optimization technique known as “bound optimization.” …
PSYC696B: Analyzing Neural Time-series Data - University of …
Oscillations are a fundamental neural mechanism that supports aspects of synaptic, cellular, and systems-level brain function across multiple spatial and temporal scales (Cohen, 2014)
Statistical modeling and analysis of neural data (NEU 560)
(aka “complete-data log-likelihood”) Two useful forms: expected total-data log-li entropy of q(z) Statistical Modeling and Analysis of Neural Data (NEU 560) Princeton University, Spring 2018 …
Emerging techniques in statistical analysis of neural data
Availability of these new data has in turn led to the emergence of a new field of computational and data-intensive neuroscience.
Statistical analysis of neural data - sites.stat.columbia.edu
HMMs have widespread applications in time-series analysis, notably in speech processing, bioinformatics, and control theory, and we will describe a wide variety of applications in …
Statistical modeling and analysis of neural data (NEU 560)
Statistical modeling and analysis of neural data (NEU 560) Jonathan Pillow Fall 2020 1
Statistical analysis of neural data - sites.stat.columbia.edu
Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves. Biological Cybernetics, 99:361–370. Badel, L., Lefort, S., Brette, R., Petersen, C. C. H., …
Statistical analysis of neural data - sites.stat.columbia.edu
The neural decoding problem is a fundamental question in computational neuroscience (Rieke et al., 1997): given the observed spike trains of a population of cells whose responses are related …
Statistical Modeling and Analysis of Neural Data (NEU 560)
This course aims to introduce students to advanced statistical and machine learning methods for analyzing of neural data, with an emphasis on methods derived from regression (supervised) …
