Statistical Signal Processing for Neuroscience - Karim G. Oweiss

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Statistical Signal Processing for Neuroscience

By: Karim G. Oweiss

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Hardcover | 4 August 2010

At a Glance

Hardcover
433 Pages

Dimensions(cm)
23.5 x 18.42 x 2.54

Hardcover

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This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.

Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience.

A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community
Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research
Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems

Industry Reviews

"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content." Gyoergy Buzsaki, M.D., Ph.D. Center for Molecular and Behavioral Neuroscience Rutgers University

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Introduction
Detection and Classification of Extracellular Action Potential Recordings
Information-Theoretic Analysis of Neural Data
Identification of Nonlinear Dynamics in Neural Population Activity
Graphical Models of Functional and Effective Neuronal Connectivity
State-Space Modeling of Neural Spike Train and Behavioral Data
Neural Decoding for Motor and Communication Prostheses
Inner Products for Representation and Learning in the Spike Train Domain
Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI
Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces
Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects with Spinal Cord Injury:
Table of Contents provided by Publisher. All Rights Reserved.

Statistical Signal Processing for Neuroscience

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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