Variational bayesian inference for point process generalized linear models in neural spike trains analysis
Name
Brown-2010-Variational bayesian inference for point process.pdf
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Author(s)
Chen, Zhe
Kloosterman, Fabian
Wilson, Matthew A.
Brown, Emery N.
Date Issued
March 2010
Journal
Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Chen, Zhe et al. “Variational Bayesian Inference for Point Process Generalized Linear Models in Neural Spike Trains Analysis.” IEEE, 2010. 2086–2089. Web. © 2010 IEEE.
Version
Final published version
Abstract
Point process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation. Variational Bayesian (VB) methods provide a computationally appealing means to infer the posterior density of unknown parameters, in which conjugate priors are designed for the regression coefficients in logistic and Poisson regression. In this paper, we develop and apply VB inference for point process GLMs in neural spike train analysis. The hierarchical Bayesian framework allows us to tackle the variable selection problem. We assess and validate our methods with ensemble neuronal recordings from rat's hippocampal place cells and entorhinal cortical cells during foraging in an open field environment.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ICASSP.2010.5495095
