Variational bayesian inference for point process generalized linear models in neural spike trains analysis
Author(s)Chen, Zhe; Kloosterman, Fabian; Wilson, Matthew A.; Brown, Emery N.
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Institute of Electrical and Electronics Engineers
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.
Final published version