State Space Models And Latent Processes In The Statistical Analysis Of Neural Data

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Advanced State Space Methods for Neural and Clinical Data

Author : Zhe Chen
Publisher : Cambridge University Press
ISBN 13 : 1316352218
Total Pages : 397 pages
Book Rating : 4.3/5 (163 download)

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Book Synopsis Advanced State Space Methods for Neural and Clinical Data by : Zhe Chen

Download or read book Advanced State Space Methods for Neural and Clinical Data written by Zhe Chen and published by Cambridge University Press. This book was released on 2015-10-15 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

State-Space Models and Latent Processes in the Statistical Analysis of Neural Data

Author : Michael Vidne
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (867 download)

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Book Synopsis State-Space Models and Latent Processes in the Statistical Analysis of Neural Data by : Michael Vidne

Download or read book State-Space Models and Latent Processes in the Statistical Analysis of Neural Data written by Michael Vidne and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the fourth chapter we develop a more general approach to the state-space filtering problem. Our method solves the same recursive set of Markovian filter equations as the particle filter, but we replace all importance sampling steps with a more general Markov chain Monte Carlo (MCMC) step. Our algorithm is especially well suited for problems where the model parameters might be misspecified.

Statistical analysis of multi-cell recordings: linking population coding models to experimental data

Author : Matthias Bethge
Publisher : Frontiers E-books
ISBN 13 : 2889190129
Total Pages : 209 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Statistical analysis of multi-cell recordings: linking population coding models to experimental data by : Matthias Bethge

Download or read book Statistical analysis of multi-cell recordings: linking population coding models to experimental data written by Matthias Bethge and published by Frontiers E-books. This book was released on 2012-01-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern recording techniques such as multi-electrode arrays and 2-photon imaging are capable of simultaneously monitoring the activity of large neuronal ensembles at single cell resolution. This makes it possible to study the dynamics of neural populations of considerable size, and to gain insights into their computations and functional organization. The key challenge with multi-electrode recordings is their high-dimensional nature. Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses and their relation with external stimuli or behavioral observations. Contributions to this Research Topic should advance statistical modeling of neural populations. Questions of particular interest include: 1. What classes of statistical methods are most useful for modeling population activity? 2. What are the main limitations of current approaches, and what can be done to overcome them? 3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding? 4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations? This Research Topic is connected to a one day workshop at the Computational Neuroscience Meeting 2009 in Berlin (http://www.cnsorg.org/2009/workshops.shtml and http://www.kyb.tuebingen.mpg.de/bethge/workshops/cns2009/)

Latent Variable Models for Neural Data Analysis

Author : Maneesh Sahani
Publisher :
ISBN 13 :
Total Pages : 324 pages
Book Rating : 4.:/5 (437 download)

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Book Synopsis Latent Variable Models for Neural Data Analysis by : Maneesh Sahani

Download or read book Latent Variable Models for Neural Data Analysis written by Maneesh Sahani and published by . This book was released on 1999 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Dynamic Neuroscience

Author : Zhe Chen
Publisher : Springer
ISBN 13 : 3319719769
Total Pages : 337 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Dynamic Neuroscience by : Zhe Chen

Download or read book Dynamic Neuroscience written by Zhe Chen and published by Springer. This book was released on 2017-12-27 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Author : Fouzi Harrou
Publisher : Elsevier
ISBN 13 : 0128193662
Total Pages : 330 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches by : Fouzi Harrou

Download or read book Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches written by Fouzi Harrou and published by Elsevier. This book was released on 2020-07-03 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis

Author : Yuanjun Gao
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis by : Yuanjun Gao

Download or read book Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis written by Yuanjun Gao and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in techniques have been producing increasingly complex neural recordings, posing significant challenges for data analysis. This thesis discusses novel statistical methods for analyzing high-dimensional neural data. Part one discusses two extensions of state space models tailored to neural data analysis. First, we propose using a flexible count data distribution family in the observation model to faithfully capture over-dispersion and under-dispersion of the neural observations. Second, we incorporate nonlinear observation models into state space models to improve the flexibility of the model and get a more concise representation of the data. For both extensions, novel variational inference techniques are developed for model fitting, and simulated and real experiments show the advantages of our extensions. Part two discusses a fast region of interest (ROI) detection method for large-scale calcium imaging data based on structured matrix factorization. Part three discusses a method for sampling from a maximum entropy distribution with complicated constraints, which is useful for hypothesis testing for neural data analysis and many other applications related to maximum entropy formulation. We conclude the thesis with discussions and future works.

Modern Methodology and Applications in Spatial-Temporal Modeling

Author : Gareth William Peters
Publisher : Springer
ISBN 13 : 4431553398
Total Pages : 123 pages
Book Rating : 4.4/5 (315 download)

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Book Synopsis Modern Methodology and Applications in Spatial-Temporal Modeling by : Gareth William Peters

Download or read book Modern Methodology and Applications in Spatial-Temporal Modeling written by Gareth William Peters and published by Springer. This book was released on 2016-01-08 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

Fixed Interval Smoothing for State Space Models

Author : Howard L. Weinert
Publisher : Springer Science & Business Media
ISBN 13 : 1461516919
Total Pages : 126 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Fixed Interval Smoothing for State Space Models by : Howard L. Weinert

Download or read book Fixed Interval Smoothing for State Space Models written by Howard L. Weinert and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. This monograph addresses problems for which a linear stochastic state space model is available, in which case the objective is to compute the linear least-squares estimate of the state vector in a fixed interval, using observations previously collected in that interval. The author uses a geometric approach based on the method of complementary models. Using the simplest possible notation, he presents straightforward derivations of the four types of fixed-interval smoothing algorithms, and compares the algorithms in terms of efficiency and applicability. Results show that the best algorithm has received the least attention in the literature. Fixed Interval Smoothing for State Space Models: includes new material on interpolation, fast square root implementations, and boundary value models; is the first book devoted to smoothing; contains an annotated bibliography of smoothing literature; uses simple notation and clear derivations; compares algorithms from a computational perspective; identifies a best algorithm. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.

Neural Modeling Fields

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 189 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Neural Modeling Fields by : Fouad Sabry

Download or read book Neural Modeling Fields written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-04 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Neural Modeling Fields Neural modeling field (NMF) is a mathematical framework for machine learning that integrates ideas from neural networks, fuzzy logic, and model based recognition. Its acronym stands for "Neural Modeling Field." Modeling fields, modeling fields theory (MFT), and Maximum likelihood artificial neural networks (MLANS) are some of the other names that have been used to refer to this concept.At the AFRL, Leonid Perlovsky is the one responsible for developing this framework. The NMF can be understood as a mathematical description of the machinery that make up the mind. These mechanisms include ideas, feelings, instincts, imagination, reasoning, and comprehension. The NMF is organized in a hetero-hierarchical structure that contains many levels. There are concept-models that encapsulate the knowledge at each level of the NMF. These concept-models generate so-called top-down signals, which interact with input signals that come from lower levels. These interactions are governed by dynamic equations, which are responsible for driving concept-model learning, adaptation, and the development of new concept-models for better correspondence to the input, bottom-up signals. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neural modeling fields Chapter 2: Machine learning Chapter 3: Supervised learning Chapter 4: Unsupervised learning Chapter 5: Weak supervision Chapter 6: Reinforcement learning Chapter 7: Neural network Chapter 8: Artificial neural network Chapter 9: Fuzzy logic Chapter 10: Adaptive neuro fuzzy inference system (II) Answering the public top questions about neural modeling fields. (III) Real world examples for the usage of neural modeling fields in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neural modeling fields' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neural modeling fields.

Statistical Methods for Modeling Human Dynamics

Author : Sy-Miin Chow
Publisher : Routledge
ISBN 13 : 1135262586
Total Pages : 442 pages
Book Rating : 4.1/5 (352 download)

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Book Synopsis Statistical Methods for Modeling Human Dynamics by : Sy-Miin Chow

Download or read book Statistical Methods for Modeling Human Dynamics written by Sy-Miin Chow and published by Routledge. This book was released on 2011-02-25 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA. Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of: Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience data Dynamic modeling techniques for intensive repeated measurement data Panel modeling techniques for fewer time points data State-space modeling techniques for psychological data Techniques used to analyze reaction time data. Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.

Advances in Statistical Machine Learning Methods for Neural Data Science

Author : Ding Zhou
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)

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Book Synopsis Advances in Statistical Machine Learning Methods for Neural Data Science by : Ding Zhou

Download or read book Advances in Statistical Machine Learning Methods for Neural Data Science written by Ding Zhou and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovations in neural data recording techniques are revolutionizing neuroscience and presenting both challenges and opportunities for statistical data analysis. This dissertation discusses several recent advances in neural data signal processing, encoding, decoding, and dimension reduction. Chapter 1 introduces challenges in neural data science and common statistical methods used to address them. Chapter 2 develops a new method to detect neurons and extract signals from noisy calcium imaging data with irregular neuron shapes. Chapter 3 introduces a novel probabilistic framework for modeling deconvolved calcium traces. Chapter 4 proposes an improved Bayesian nonparametric extension of the hidden Markov model (HMM) that separates the strength of the self-persistence prior and transition prior. Chapter 5 introduces a more identifiable and interpretable latent variable model for Poisson observations. We develop efficient algorithms to fit each of the aforementioned methods and demonstrate their effectiveness on both simulated and real data.

Statistical Deep Learning for Spatial and Spatiotemporal Data

Author : Christopher K. Wikle
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Statistical Deep Learning for Spatial and Spatiotemporal Data by : Christopher K. Wikle

Download or read book Statistical Deep Learning for Spatial and Spatiotemporal Data written by Christopher K. Wikle and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.

Statistical Inferences of Biophysical Neural Models

Author : Liang Meng
Publisher :
ISBN 13 :
Total Pages : 354 pages
Book Rating : 4.:/5 (951 download)

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Book Synopsis Statistical Inferences of Biophysical Neural Models by : Liang Meng

Download or read book Statistical Inferences of Biophysical Neural Models written by Liang Meng and published by . This book was released on 2013 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: A fundamental issue in neuroscience is to understand the dynamic properties of, and biological mechanisms underlying, neural spiking activity. Two types of approaches have been developed: statistical and biophysical modeling. Statistical models focus on describing simple relationships between observed neural spiking activity and the signals that the brain encodes. Biophysical models concentrate on describing the biological mechanisms underlying the generation of spikes. Despite a large body of work, there remains an unbridged gap between the two model types.In this thesis, we propose a statistical framework linking observed spiking patterns to a general class of dynamic neural models. The framework uses a sequential Monte Carlo, or particle filtering, method to efficiently explore the parameter space of a detailed dynamic or biophysical model. We utilize point process theory to develop a procedure for estimating parameters and hidden variables in neuronal biophysical models given only the observed spike times. We successfully implement this method for simulated examples and address the issues of model identification and misspecification.We then apply the particle filter to actual spiking data recorded from rat layer V cortical neurons and show that it correctly identifies the dynamics of a non-traditional, intrinsic current. The method succeeds even though the observed cells exhibit two distinct classes of spiking activity: regular spiking and bursting. We propose that the approach can also frame hypotheses of underlying intrinsic currents that can be directly tested by current or future biological and/or psychological experiments.We then demonstrate the application of the proposed method to a separate problem: constructing a hypothesis test to investigate whether a point process is generated by a constant or dynamically varying intensity function. The hypothesis is formulated as an autoregressive state space model, which reduces the testing problem to a test on the variance of the state process. We apply the particle filtering method to compute test statistics and identify the rejection region. A simulation study is performed to quantify the power of this test procedure.Ultimately, the modeling framework and estimation procedures we developed provide a successful link between dynamical neural models and statistical inference from spike train data

Practical Algorithms for Latent Variable Models

Author : Gregory W. Gundersen
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.4/5 (711 download)

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Book Synopsis Practical Algorithms for Latent Variable Models by : Gregory W. Gundersen

Download or read book Practical Algorithms for Latent Variable Models written by Gregory W. Gundersen and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Latent variables allow researchers and engineers to encode assumptions into their statistical models. A latent variable might, for example, represent an unobserved covariate, measurement error, or a missing class label. Inference is challenging because one must account for the conditional dependence structure induced by these variables, and marginalization is often intractable. In this thesis, I present several practical algorithms for inferring latent structure in probabilistic models used in computational biology, neuroscience, and time-series analysis.First, I present a multi-view framework that combines neural networks and probabilistic canonical correlation analysis to estimate shared and view-specific latent structure of paired samples of histological images and gene expression levels. The model is trained end-to-end to estimate all parameters simultaneously, and we show that the latent variables capture interpretable structure, such as tissue-specific and morphological variation. Next, I present a family of nonlinear dimension-reduction models that use random features to support non-Gaussian data likelihoods. By approximating a nonlinear relationship between the latent variables and observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variables. This allows for gradient-based nonlinear dimension-reduction models for a variety of data likelihoods. Finally, I discuss lowering the computational cost of online Bayesian filtering of time series with abrupt changes in structure, called changepoints. We consider settings in which a time series has multiple data sources, each with an associated cost. We trade the cost of a data source against the quality or fidelity of that source and how its fidelity affects the estimation of changepoints. Our framework makes cost-sensitive decisions about which data source to use based on minimizing the information entropy of the posterior distribution over changepoints.

Longitudinal Models in the Behavioral and Related Sciences

Author : Kees van Montfort
Publisher : Routledge
ISBN 13 : 1351559745
Total Pages : 450 pages
Book Rating : 4.3/5 (515 download)

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Book Synopsis Longitudinal Models in the Behavioral and Related Sciences by : Kees van Montfort

Download or read book Longitudinal Models in the Behavioral and Related Sciences written by Kees van Montfort and published by Routledge. This book was released on 2017-09-29 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available. Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications such as heterogeneity on the patterns of a firm’s profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. This new volume is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.

State Space Methods Using Biologically-relevant Generative Models to Analyze Neural Signals

Author : Amanda M. Beck
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis State Space Methods Using Biologically-relevant Generative Models to Analyze Neural Signals by : Amanda M. Beck

Download or read book State Space Methods Using Biologically-relevant Generative Models to Analyze Neural Signals written by Amanda M. Beck and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.