Download or read book Statistical Models of Neural Coding in Motor Cortex written by Wei Wu and published by . This book was released on 2004 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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/)
Download or read book Neural Coding and the Statistical Modeling of Neuronal Responses written by Jonathan Pillow and published by . This book was released on 2005 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Download or read book A Principled Statistical Analysis of Discrete Context dependent Neural Coding written by Yifei Huang and published by . This book was released on 2010 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The analysis of neural data from the brain, and in particular the hippocampus, which plays an active role in learning and memory, presents multiple statistical challenges. First of all, neurons communicate via stereotyped electrical impulses, or spikes, that are localized in time, and are most appropriately described by point processes. These spikes may be related to a multitude of continuous-valued or discrete biological and behavioral signals. Finally, new technologies allow simultaneous recording from hundreds of neurons. Establishing stochastic models relating thee signals requires principled statistical methods. In this thesis, we establish a statistical modeling, estimation and hypothesis testing framework for hippocampal neural spiking data, based on point process theory. The fundamental component of this framework lies in the construction of a probability model based on the conditional intensity function (CIF), which uniquely characterizes a point process. We express the CIF as a function of biological and behavioral covariates that influence neural spiking. This allows us to compute likelihoods, fit models, and perform goodness-of-fit analyses. We develop a hypothesis-testing framework based on the fit models, and build sampling distributions for the test statistics. Finally, we develop adaptive estimation algorithms to reconstruct and predict behavior from spiking data. We apply this framework to the analysis of spiking data from rat hippocampus, while the animal performed a spatial-navigation task on a T-shaped maze. We apply the point process estimation algorithm to reconstruct the animal's movement through the maze and predict future turn directions. We employ the hypothesis-testing framework to compare firing activity preceding different turn directions. Finally, we investigate the statistical relationship of spiking data to oscillatory neural rhythms. These analyses provide a deeper understanding of how the hippocampus maintains representations of spatial signals during memory related tasks.
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.
Download or read book Neural Coding of Motor Performance written by J. Massion and published by Springer. This book was released on 1983 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Modeling and Applications of Neural Spike Trains written by Vernon Lawhern and published by . This book was released on 2011 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: In this thesis we investigate statistical modelling of neural activity in the brain. We rst develop a framework which is an extension of the state-space Generalized Linear Model (GLM) by Eden and colleagues [20] to include the eects of hidden states. These states, collectively, represent variables which are not observed (or even observable) in the modelling process but nonetheless can have an impact on the neural activity. We then develop a framework that allows us to input apriori target information into the model. We examine both of these modelling frameworks on motor cortex data recorded from monkeys performing dierent target-driven hand and arm movement tasks. Finally, we perform temporal coding analysis of sensory stimulation using principled statistical models and show the ecacy of our approach.
Download or read book Principles of Neural Coding written by Rodrigo Quian Quiroga and published by CRC Press. This book was released on 2013-05-06 with total page 643 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc). Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states—such as local field potentials or current source densities—is the basis of the introductory chapters. Provides a comprehensive and interdisciplinary approach Describes topics of interest to a wide range of researchers The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.
Download or read book Neural Codes and Distributed Representations written by L. F. Abbott and published by MIT Press. This book was released on 1999 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.
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: An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Download or read book Probabilistic Neural Coding from Deterministic Neural Dynamics written by Michael G. Famulare and published by . This book was released on 2012 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: The basic unit of computation in the nervous system is the transformation of input into output spikes performed by an individual neuron. The spiking response of the neuron to a complex, time-varying input can be characterized with two different classes of models: nonlinear dynamical systems represent the detailed biophysical properties a neuron, and probabilistic black box coding models identify abstract representations of the computation performed. However, the relationships between biophysical mechanisms and neural coding properties have very rarely been resolved. Here, the focus is on the task of feature selection, where a neuron extracts and encodes from its complex inputs a small number of relevant signal components. Feature selection is generally adaptive: both the relevant features and the encoding depend on the background statistical context in which the signal appears. This thesis presents a theory of conditional dynamical processes that associate abstract representations of the signal with sub-ensembles of states of the corresponding dynamical system. The theory provides a bridge to use meth- ods from either coding or dynamics to simultaneously study both. The unifying framework is used to derive how the interactions of the statistical properties of the input and the neural dynamics determine which features of the input are encoded by spikes. Adaptation of the encoding to changes in input statistics is shown to arise from corresponding changes in how the state space of the nonlinear system is probed by the input. First, we identify the mechanisms of adaptive feature selection in integrate-and-fire mod- els. Then, we demonstrate that integrate-and-fire models without any additional currents can perform a novel type of stochastically-emergent perfect contrast gain control--a sophis- ticated adaptive computation. We identify the general dynamical principles responsible and design from first principles a nonlinear dynamical model that implements automatic gain control. We conclude by fitting models to experimental data and relating the models to measurable biophysical properties to demonstrate that our proposed theoretical mechanism is consistent with the adaptive gain control observed in the developing cortex.
Download or read book Statistical Methods for Studying Neural Coding written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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
Download or read book Principles of Neural Coding written by Rodrigo Quian Quiroga and published by CRC Press. This book was released on 2013-05-06 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this b
