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An Overview of Bayesian Methods for Neural Spike Train Analysis
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-17
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits.
With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity.
Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels.
On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation.
On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony.
Some research challenges and opportunities for neural spike train analysis are discussed.
American Psychological Association (APA)
Chen, Zhe. 2013. An Overview of Bayesian Methods for Neural Spike Train Analysis. Computational Intelligence and Neuroscience،Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-457545
Modern Language Association (MLA)
Chen, Zhe. An Overview of Bayesian Methods for Neural Spike Train Analysis. Computational Intelligence and Neuroscience No. 2013 (2013), pp.1-17.
https://search.emarefa.net/detail/BIM-457545
American Medical Association (AMA)
Chen, Zhe. An Overview of Bayesian Methods for Neural Spike Train Analysis. Computational Intelligence and Neuroscience. 2013. Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-457545
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457545