Neural Comput - On quadrature methods for refractory point process likelihoods.

Tópicos

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Resumo

Parametric models of the conditional intensity of a point process (e.g., generalized linear models) are popular in statistical neuroscience, as they allow us to characterize the variability in neural responses in terms of stimuli and spiking history. Parameter estimation in these models relies heavily on accurate evaluations of the log likelihood and its derivatives. Classical approaches use a discretized time version of the spiking process, and recent work has exploited the existence of a refractory period (during which the conditional intensity is zero following a spike) to obtain more accurate estimates of the likelihood. In this brief letter, we demonstrate that this method can be improved significantly by applying classical quadrature methods directly to the resulting continuous-time integral.

Resumo Limpo

parametr model condit intens point process eg general linear model popular statist neurosci allow us character variabl neural respons term stimuli spike histori paramet estim model reli heavili accur evalu log likelihood deriv classic approach use discret time version spike process recent work exploit exist refractori period condit intens zero follow spike obtain accur estim likelihood brief letter demonstr method can improv signific appli classic quadratur method direct result continuoustim integr

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