Estimating Parameters of Generalized Integrate-and-Fire Neurons from the Maximum Likelihood of Spike Trains

Yi Dong, Stefan Mihalas, Alexander Russell, Ralph Etienne-Cummings, Ernst Niebur
2011 Neural Computation  
Y. Dong et al. methods to be used. In this study, we show that although convexity of the negative log-likelihood function is not guaranteed for this model, the minimum of this function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) usually reaches the global minimum.
doi:10.1162/neco_a_00196 pmid:21851282 pmcid:PMC3513351 fatcat:cbph3v262bbf7jllt6lutow7hy