An Efficient and Flexible Spike Train Model via Empirical Bayes

no code implementations • 10 May 2016 • Qi She, Xiaoli Wu, Beth Jelfs, Adam S. Charles, Rosa H. M. Chan

Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations.

Bayesian Inference