An Efficient and Flexible Spike Train Model Via Empirical Bayes

@article{She2021AnEA,
  title={An Efficient and Flexible Spike Train Model Via Empirical Bayes},
  author={Qi She and Xiaoli Wu and Beth Jelfs and Adam S. Charles and Rosa H.M.Chan},
  journal={IEEE Transactions on Signal Processing},
  year={2021},
  volume={69},
  pages={3236-3251}
}
Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neurons demonstrate over-dispersed spiking behaviour. Although the Negative Binomial Generalized Linear Model (NB-GLM) provides a powerful tool for modeling over-dispersed spike… 

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