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Electrophysiology Analysis, Bayesian

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Macke,  JH
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Macke, J. (2014). Electrophysiology Analysis, Bayesian. In D. Jaeger R. Jung (Ed.), Encyclopedia of Computational Neuroscience (pp. 1-5). New York, NY, USA: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-80CE-2
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.