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Stochastic Processes in the Neurosciences

Stochastic Processes in the Neurosciences PDF Author: Henry C. Tuckwell
Publisher: SIAM
ISBN: 0898712327
Category : Technology & Engineering
Languages : en
Pages : 128

Book Description
This monograph is centered on quantitative analysis of nerve-cell behavior. The work is foundational, with many higher order problems still remaining, especially in connection with neural networks. Thoroughly addressed topics include stochastic problems in neurobiology, and the treatment of the theory of related Markov processes.

Stochastic Processes in the Neurosciences

Stochastic Processes in the Neurosciences PDF Author: Henry C. Tuckwell
Publisher: SIAM
ISBN: 0898712327
Category : Technology & Engineering
Languages : en
Pages : 128

Book Description
This monograph is centered on quantitative analysis of nerve-cell behavior. The work is foundational, with many higher order problems still remaining, especially in connection with neural networks. Thoroughly addressed topics include stochastic problems in neurobiology, and the treatment of the theory of related Markov processes.

Stochastic Processes in the Neurosciences

Stochastic Processes in the Neurosciences PDF Author: Henry C. Tuckwell
Publisher: SIAM
ISBN: 9781611970159
Category : Technology & Engineering
Languages : en
Pages : 134

Book Description
This monograph is centered on quantitative analysis of nerve-cell behavior. The work is foundational, with many higher order problems still remaining, especially in connection with neural networks. Thoroughly addressed topics include stochastic problems in neurobiology, and the treatment of the theory of related Markov processes.

Stochastic Methods in Neuroscience

Stochastic Methods in Neuroscience PDF Author: Carlo Laing
Publisher: Oxford University Press
ISBN: 0199235074
Category : Mathematics
Languages : en
Pages : 399

Book Description
Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis. These techniques are leading to biophysically more realistic models. It has also become clear that both neuroscientists and mathematicians profit from collaborations in this exciting research area.Graduates and researchers in computational neuroscience and stochastic systems, and neuroscientists seeking to learn more about recent advances in the modelling and analysis of noisy neural systems, will benefit from this comprehensive overview. The series of self-contained chapters, each written by experts in their field, covers key topics such as: Markov chain models for ion channel release; stochastically forced single neurons and populations of neurons; statistical methods for parameterestimation; and the numerical approximation of these stochastic models.Each chapter gives an overview of a particular topic, including its history, important results in the area, and future challenges, and the text comes complete with a jargon-busting index of acronyms to allow readers to familiarize themselves with the language used.

Stochastic Neuron Models

Stochastic Neuron Models PDF Author: Priscilla E. Greenwood
Publisher: Springer
ISBN: 3319269119
Category : Mathematics
Languages : en
Pages : 82

Book Description
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.

Stochastic Biomathematical Models

Stochastic Biomathematical Models PDF Author: Mostafa Bachar
Publisher: Springer
ISBN: 3642321577
Category : Mathematics
Languages : en
Pages : 216

Book Description
Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

Some Stochastic Processes Arising in Neurobiology

Some Stochastic Processes Arising in Neurobiology PDF Author: Ian William Saunders
Publisher:
ISBN:
Category : Neurobiology
Languages : en
Pages : 220

Book Description


Introduction to Theoretical Neurobiology: Volume 2, Nonlinear and Stochastic Theories

Introduction to Theoretical Neurobiology: Volume 2, Nonlinear and Stochastic Theories PDF Author: Henry C. Tuckwell
Publisher: Cambridge University Press
ISBN: 9780521352178
Category : Mathematics
Languages : en
Pages : 292

Book Description
The second part of this two-volume set contains advanced aspects of the quantitative theory of the dynamics of neurons. It begins with an introduction to the effects of reversal potentials on response to synaptic input. It then develops the theory of action potential generation based on the seminal Hodgkin-Huxley equations and gives methods for their solution in the space-clamped and nonspaceclamped cases. The remainder of the book discusses stochastic models of neural activity and ends with a statistical analysis of neuronal data with emphasis on spike trains. The mathematics is more complex in this volume than in the first volume and involves numerical methods of solution of partial differential equations and the statistical analysis of point processes.

Phase Resetting in Medicine and Biology

Phase Resetting in Medicine and Biology PDF Author: Peter A. Tass
Publisher: Springer
ISBN: 9783540656975
Category : Science
Languages : en
Pages : 0

Book Description
This book presents a new theoretical approach to phase resetting and stimulation-induced synchronization and desynchronization in a population of oscillators. The author uses stochastic methods from statistical mechanics and applies his theory to models of practical importance in physiology and neuroscience. The book is accessible to readers not familiar with the mathematical formalism. The author also proposes improvements to stimulation techniques as used by neurologists and neurosurgeons in the context of Parkinson's disease and MEG/EEG data analysis.

Stochastic Cellular Systems

Stochastic Cellular Systems PDF Author: R. L. Dobrushin
Publisher: Manchester University Press
ISBN: 9780719022067
Category : Cellular automata
Languages : en
Pages : 568

Book Description


Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems

Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems PDF Author: M. Reza Rahimi Tabar
Publisher: Springer
ISBN: 3030184722
Category : Science
Languages : en
Pages : 280

Book Description
This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.