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  Electrophysiology Analysis, Bayesian

Macke, J. (2015). Electrophysiology Analysis, Bayesian. In D. Jaeger, & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1078-1082). New York, NY, USA: Springer.

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 Creators:
Macke, JH1, 2, Author           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Bayesian analysis of electrophysiological data refers to the statistical processing of data obtained in electrophysiological experiments (i.e., recordings of action potentials or voltage measurements with electrodes or imaging devices) which utilize methods from Bayesian statistics. Bayesian statistics is a framework for describing and modelling empirical data using the mathematical language of probability to model uncertainty. Bayesian statistics provides a principled and flexible framework for combining empirical observations with prior knowledge and for quantifying uncertainty. These features are especially useful for analysis questions in which the dataset sizes are small in comparison to the complexity of the model, which is often the case in neurophysiological data analysis.

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 Dates: 2015
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-1-4614-7320-6_448-1
BibTex Citekey: Macke2014
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Title: Encyclopedia of Computational Neuroscience
Source Genre: Book
 Creator(s):
Jaeger, D, Editor
Jung, R, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1078 - 1082 Identifier: ISBN: 978-1-4614-6674-1