Definition
Bayesian approaches in Computational Neuroscience rely on the properties of Bayesian statistics for performing inference over unknown variables given a data set generated through a stochastic process.
Detailed Description
Given a set of observed data d1:n, generated from a stochastic process P(d1:n|X) where X is a set of unobserved variables, the posterior probability distribution of X is P(X|d1:n) = P(d1:n|X)P(X)/P(d1:n) according to Bayes’ theorem. X can be a set of fixed parameters as well as a series of variables of the same size as the data itself X1:n.
Based on the posterior probability and a specified utility function, an estimate of X can be made that can be shown to be optimal, for example, by minimizing the expected variance.
One common use of this principle within computational neuroscience is for inferring unobserved properties (hidden variables X) based on observed data, d. These techniques can be used for inference on any data sets but has in neuroscience...
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Beierholm, U.R. (2020). Bayesian Approaches in Computational Neuroscience: Overview. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_778-3
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_778-3
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7320-6
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences
Publish with us
Chapter history
-
Latest
Bayesian Approaches in Computational Neuroscience: Overview- Published:
- 03 August 2020
DOI: https://doi.org/10.1007/978-1-4614-7320-6_778-3
-
Original
Bayesian Approaches in Computational Neuroscience: Overview- Published:
- 29 March 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_778-2