[PDF] State Space Methods Using Biologically Relevant Generative Models To Analyze Neural Signals eBook

Author : Amanda M. Beck
Publisher :
Page : 0 pages
File Size : 18,77 MB
Release : 2023
Category :
ISBN :

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Neural oscillations have long been recognized for their mechanistic importance in coordinating activity within and between brain circuits. Co-occurring broad-band, non-periodic signals are also ubiquitous in neural data and are thought to reflect the characteristics of population-level neuronal spiking activity. Identifying oscillatory activity distinct from broadband signals is therefore an important, yet surprisingly difficult, problem in neuroscience. Commonly-used bandpass filters produce spurious oscillations when applied to broad-band noise and may be ill-informed by canonical frequency bands. Curve-fitting procedures have been developed to identify peaks in the power spectrum distinct from broadband noise. Unfortunately, these ad hoc methods are prone to overfitting and are difficult to interpret in the absence of generative models to formally represent oscillatory behavior. Similarly, broadband power spectrum log-log slope or "1/f" curve-fitting methods have been developed to identify excitatory-inhibitory balance in the LFP or ECoG, but are not defined in terms of a generative model. Here we present three novel methods that utilize generative models to (1) identify and characterize neural oscillations distinct from broad-band noise (2) apply this oscillatory structure to improve cortical source signal estimates inferred from scalp-level EEG recordings and (3) identify and characterize excitatory and inhibitory neurotransmitter contributions to LFP signals.

Author : Zhe Chen
Publisher : Cambridge University Press
Page : 397 pages
File Size : 16,9 MB
Release : 2015-10-15
Category : Computers
ISBN : 1107079195

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An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

Author : Mark A. Kramer
Publisher : MIT Press
Page : 385 pages
File Size : 30,8 MB
Release : 2016-11-04
Category : Science
ISBN : 0262529378

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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.

Author : Andrew Hyungsuk Song
Publisher :
Page : 0 pages
File Size : 37,65 MB
Release : 2022
Category :
ISBN :

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When I initially set out to research in the intersection of statistical signal processing and neuroscience (neural signal processing), my research advisor, Professor Emery N. Brown, explained at length that the signals from seemingly complex neural/biological systems are not purely random, but rather those that have latent structures that can be recovered with principled approaches. This insight has stuck with me since that moment and my research throughout graduate school has been understanding and practicing what I thought was the appropriate neural signal processing framework. In this thesis, I define this framework from the Bayesian/optimization perspective and emphasize translating and integrating the clinical and scientific domain knowledge, obtained from constant interaction/collaboration with the experimental neuroscientists and clinicians. The thesis specifically focuses on uncovering latent structures in the neural time series data, by using domain priors/constraints, such as Gaussian process, shift-invariance, sparsity, and smoothness, among many others. It is demonstrated in the thesis that the Bayesian approach with careful integration of these constraints produces results/structures in the data that are not only intepretable but also better performing for the metrics of interest.

Author : K. Gal
Publisher : IOS Press
Page : 3328 pages
File Size : 24,99 MB
Release : 2023-10-18
Category : Computers
ISBN : 164368437X

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Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Author : Gerald Young
Publisher : Springer
Page : 962 pages
File Size : 48,39 MB
Release : 2016-05-17
Category : Psychology
ISBN : 3319240943

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This magistral treatise approaches the integration of psychology through the study of the multiple causes of normal and dysfunctional behavior. Causality is the focal point reviewed across disciplines. Using diverse models, the book approaches unifying psychology as an ongoing project that integrates genetics, experience, evolution, brain, development, change mechanisms, and so on. The book includes in its integration free will, epitomized as freedom in being. It pinpoints the role of the self in causality and the freedom we have in determining our own behavior. The book deals with disturbed behavior, as well, and tackles the DSM-5 approach to mental disorder and the etiology of psychopathology. Young examines all these topics with a critical eye, and gives many innovative ideas and models that will stimulate thinking on the topic of psychology and causality for decades to come. It is truly integrative and original. Among the topics covered: Models and systems of causality of behavior. Nature and nurture: evolution and complexities. Early adversity, fetal programming, and getting under the skin. Free will in psychotherapy: helping people believe. Causality in psychological injury and law: basics and critics. A Neo-Piagetian/Neo-Eriksonian 25-step (sub)stage model. Unifying Causality and Psychology appeals to the disciplines of psychology, psychiatry, epidemiology, philosophy, neuroscience, genetics, law, the social sciences and humanistic fields, in general, and other mental health fields. Its level of writing makes it appropriate for graduate courses, as well as researchers and practitioners.

Author : Gisbert Schneider
Publisher : Wiley-VCH
Page : 0 pages
File Size : 12,5 MB
Release : 2013-12-23
Category : Medical
ISBN : 9783527334612

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Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.

Author :
Publisher :
Page : 812 pages
File Size : 27,80 MB
Release : 2005
Category : Biology
ISBN :

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Each issue of Transactions B is devoted to a specific area of the biological sciences, including clinical science. All papers are peer reviewed and edited to the highest standards. Published on the 29th of each month, Transactions B is essential reading for all biologists.

Author : Ian L. Dryden
Publisher : John Wiley & Sons
Page : 496 pages
File Size : 49,26 MB
Release : 2016-06-28
Category : Mathematics
ISBN : 1119072506

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A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .