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Statistical Inferences Of Biophysical Neural Models
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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
Book Synopsis Simulation-based Inference for Neuroscience and Beyond by : Jan-Matthis Lückmann
Download or read book Simulation-based Inference for Neuroscience and Beyond written by Jan-Matthis Lückmann and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Science makes extensive use of simulations to model the world. Statistical inference identifies which models are consistent with observed phenomena, thus bridging the gap between theory and reality. However, conventional statistical inference is often inapplicable to detailed simulation models because their associated likelihood functions are intractable. Simulation-based inference (SBI) addresses this problem: It allows statistical inference from simulations alone and can thus be used with implicit models, which lack evaluable likelihoods. This thesis consists of four publications that draw on advances in machine learning to contribute to the transition away from heuristic approaches towards principled statistical inference with SBI, which allows to identify data-consistent models. To this end, this thesis proposes new algorithms, applications to neuroscience, and the first unified benchmark for SBI. Overall, it shows the potential for fast and flexible likelihood-free algorithms to facilitate scientific discovery in neuroscience and beyond. The trade-off between models of neural dynamics that are statistically amenable or mechanistically plausible was the starting point for the work presented in this thesis. In the first publication, we introduce an SBI algorithm for sequential neural posterior estimation, which overcomes the drawbacks of an earlier method. We provide several extensions motivated by challenging problems in neuroscience, including end-to-end learning of summary statistics for high-dimensional time series data. In the second publication, we demonstrate its broad applicability to mechanistic models in neuroscience--from the scale of ion channels, which are the basic building blocks of biophysical neuron models, to network models of neural dynamics. Our approach overcomes the limitations of heuristic alternatives and narrows the divide between statistical and mechanistic models. The third publication proposes a novel SBI algorithm that proceeds by learning an emulator of the simulator. This approach enables the use of active learning schemes to adaptively acquire new simulations, which allows scaling to problems that are computationally highly expensive. With rapid progress in SBI, the need for a unified benchmark became apparent: In the fourth publication, we propose the first benchmark for the field to transparently evaluate progress and to contribute to more efficient and reproducible science.
Book Synopsis Neuronal Dynamics by : Wulfram Gerstner
Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin–Huxley equations and Hopfield model, as well as modern developments in the field such as generalized linear models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.
Book Synopsis Analysis and Modeling of Neural Systems by : Frank H. Eeckman
Download or read book Analysis and Modeling of Neural Systems written by Frank H. Eeckman and published by Springer Science & Business Media. This book was released on 2012-02-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: I - Analysis and Modeling Tools and Techniques.- Section 1: Analysis.- Assembly Connectivity and Activity: Methods, Results, Interpretations.- Visualization of Cortical Connections With Voltage Sensitive Dyes.- Channels, Coupling, and Synchronized Rhythmic Bursting Activity.- Sparse-stimulation and Wiener Kernels.- Quantitative Search for Stimulus-Specific Patterns in the Human Electroencephalogram (EEG) During a Somatosensory Task.- Section 2: Modeling.- Functional Insights About Synaptic Inputs to Dendrites.- Dendritic Control of Hebbian Computations.- Low Threshold Spikes and Rhythmic Oscil.
Book Synopsis Statistical Parametric Mapping: The Analysis of Functional Brain Images by : William D. Penny
Download or read book Statistical Parametric Mapping: The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. An essential reference and companion for users of the SPM software Provides a complete description of the concepts and procedures entailed by the analysis of brain images Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data Stands as a compendium of all the advances in neuroimaging data analysis over the past decade Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes Structured treatment of data analysis issues that links different modalities and models Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
Book Synopsis Neural Systems: Analysis and Modeling by : Frank H. Eeckman
Download or read book Neural Systems: Analysis and Modeling written by Frank H. Eeckman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental techniques have enabled biologists to gather the data necessary to test these new theories. While we do not yet understand how the brain sees, hears or smells, we do have testable models of specific components of visual, auditory, and olfactory processing. Some of these models have been applied to help construct artificial vision and hearing systems. Similarly, our understanding of motor control has grown to the point where it has become a useful guide in the development of artificial robots. Many neuroscientists believe that we have only scratched the surface, and that a more complete understanding of biological information processing is likely to lead to technologies whose impact will propel another industrial revolution. Neural Systems: Analysis and Modeling contains the collected papers of the 1991 Conference on Analysis and Modeling of Neural Systems (AMNS), and the papers presented at the satellite symposium on compartmental modeling, held July 23-26, 1992, in San Francisco, California. The papers included, present an update of the most recent developments in quantitative analysis and modeling techniques for the study of neural systems.
Book Synopsis Spiking Neuron Models by : Wulfram Gerstner
Download or read book Spiking Neuron Models written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2002-08-15 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Computational Modeling of Neural Activities for Statistical Inference by : Antonio Kolossa
Download or read book Computational Modeling of Neural Activities for Statistical Inference written by Antonio Kolossa and published by Springer. This book was released on 2016-05-12 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field.
Book Synopsis Exploiting Biology's Structure -- by : Barbara Ann Wendelberger
Download or read book Exploiting Biology's Structure -- written by Barbara Ann Wendelberger and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology consistently demonstrates the correlation between anatomical structure and physiological function. Blood oxygenation levels measured in functional magnetic resonance imaging (fMRI) are used to infer regions of functional activation in the brain's gray matter, while measured water diffusion in diffusion tensor imaging (DTI) can be used to infer the structural location of myelinated white matter tracts. Effective connectivity modeling in neuroimaging, which estimates directed neural network models, has historically focused almost exclusively on the analysis of fMRI. The well-established association between anatomy and physiology suggests that incorporating structural information into functional data models could improve both the estimation and understanding of neurobiological networks. In neuroimaging, this idea can be tested by combining structural information from DTI with fMRI to investigate effective connectivity estimates using dynamic causal modeling (DCM). DCM incorporates statistical inference with biophysical modeling to estimate directed neural networks from fMRI data using an input-state-output model and a fully Bayesian approach. Default DCM analyses in Matlab utilize Gaussian shrinkage priors in the initialization of the neuronal state equations. A previous study suggests that increasing the variance of these shrinkage priors, based on the probability of an anatomical connection, improves the functional MRI effective connectivity estimates, as determined by Bayesian model selection (BMS). The statistical methods presented here explore the impact of the DCM prior means on neuronal connectivity estimates and investigate the degree to which DCMs might profit from the inclusion of detailed quantitative anatomical connectivity knowledge. Modeling that more accurately represents both the brain's anatomy and its physiology will improve the estimates of and inferences on brain connectivity networks. Further understanding of network connectivity in the brain paves the way for more effective treatments and therapies aimed at improving patients' quality of life, particularly in pathological situations.
Book Synopsis Bayesian Methods in Structural Bioinformatics by : Thomas Hamelryck
Download or read book Bayesian Methods in Structural Bioinformatics written by Thomas Hamelryck and published by Springer. This book was released on 2012-03-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
Book Synopsis Exploiting Biology's Structure – Function Relationship to Improve Effective Connectivity Estimates in Neuroimaging by :
Download or read book Exploiting Biology's Structure – Function Relationship to Improve Effective Connectivity Estimates in Neuroimaging written by and published by . This book was released on 2016 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology consistently demonstrates the correlation between anatomical structure and physiological function. Blood oxygenation levels measured in functional magnetic resonance imaging (fMRI) are used to infer regions of functional activation in the brain’s gray matter, while measured water diffusion in diffusion tensor imaging (DTI) can be used to infer the structural location of myelinated white matter tracts. Effective connectivity modeling in neuroimaging, which estimates directed neural network models, has historically focused almost exclusively on the analysis of fMRI. The well-established association between anatomy and physiology suggests that incorporating structural information into functional data models could improve both the estimation and understanding of neurobiological networks. In neuroimaging, this idea can be tested by combining structural information from DTI with fMRI to investigate effective connectivity estimates using dynamic causal modeling (DCM). DCM incorporates statistical inference with biophysical modeling to estimate directed neural networks from fMRI data using an input-state-output model and a fully Bayesian approach. Default DCM analyses in Matlab utilize Gaussian shrinkage priors in the initialization of the neuronal state equations. A previous study suggests that increasing the variance of these shrinkage priors, based on the probability of an anatomical connection, improves the functional MRI effective connectivity estimates, as determined by Bayesian model selection (BMS). The statistical methods presented here explore the impact of the DCM prior means on neuronal connectivity estimates and investigate the degree to which DCMs might profit from the inclusion of detailed quantitative anatomical connectivity knowledge. Modeling that more accurately represents both the brain’s anatomy and its physiology will improve the estimates of and inferences on brain connectivity networks. Further understanding of network connectivity in the brain paves the way for more effective treatments and therapies aimed at improving patients’ quality of life, particularly in pathological situations.
Book Synopsis Analysis of Neural Data by : Robert E. Kass
Download or read book Analysis of Neural Data written by Robert E. Kass and published by Springer. This book was released on 2014-07-08 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Approximate Bayesian Computation by : Scott A. Sisson
Download or read book Handbook of Approximate Bayesian Computation written by Scott A. Sisson and published by CRC Press. This book was released on 2018-09-03 with total page 679 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement. The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.
Book Synopsis Neural Modeling by : Ronald MacGregor
Download or read book Neural Modeling written by Ronald MacGregor and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce and survey the various quantitative methods which have been proposed for describing, simulating, embodying, or characterizing the processing of electrical signals in nervous systems. We believe that electrical signal processing is a vital determinant of the functional organization of the brain, and that in unraveling the inherent complexities of this processing it will be essential to utilize the methods of quantification and modeling which have led to crowning successes in the physical and engineering sciences. In comprehensive terms, we conceive neural modeling to be the attempt to relate, in nervous systems, function to structure on the basis of operation. Sufficient knowledge and appropriate tools are at hand to maintain a serious and thorough study in the area. However, work in the area has yet to be satisfactorily integrated within contemporary brain research. Moreover, there exists a good deal of inefficiency within the area resulting from an overall lack of direction, critical self-evaluation, and cohesion. Such theoretical and modeling studies as have appeared exist largely as fragmented islands in the literature or as sparsely attended sessions at neuroscience conferences. In writing this book, we were guided by three main immediate objectives. Our first objective is to introduce the area to the upcoming generation of students of both the hard sciences and psychological and biological sciences in the hope that they might eventually help bring about the contributions it promises.
Book Synopsis Statistical and Process Models for Cognitive Neuroscience and Aging by : Michael J. Wenger
Download or read book Statistical and Process Models for Cognitive Neuroscience and Aging written by Michael J. Wenger and published by Psychology Press. This book was released on 2007-01-30 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical and Process Models for Cognitive Neuroscience and Aging addresses methodological techniques for researching cognitive impairment, Alzheimer's disease, the biophysics and structure of the nervous system, the physiology of memory, and the analysis of EEG data. Each chapter, written by the expert in the area, provides a carefully crafted i
Book Synopsis Advances on Methodological and Applied Aspects of Probability and Statistics by : N. Balakrishnan
Download or read book Advances on Methodological and Applied Aspects of Probability and Statistics written by N. Balakrishnan and published by CRC Press. This book was released on 2004-03-01 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is one of two volumes that sets forth invited papers presented at the International Indian Statistical Association Conference. This volume emphasizes advancements in methodology and applications of probability and statistics. The chapters, representing the ideas of vanguard researchers on the topic, present several different subspecialties, including applied probability, models and applications, estimation and testing, robust inference, regression and design and sample size methodology. The text also fully describes the applications of these new ideas to industry, ecology, biology, health, economics and management. Researchers and graduate students in mathematical analysis, as well as probability and statistics professionals in industry, will learn much from this volume.
Book Synopsis Stochastic Neuron Models by : Priscilla E. Greenwood
Download or read book Stochastic Neuron Models written by Priscilla E. Greenwood and published by Springer. This book was released on 2016-02-02 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain Research Centre at the University of British Columbia.
