Qi She

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The amount of publicly accessible experimental data has gradually increased in recent years, which makes it possible to reconsider many longstanding questions in neuroscience. In this paper, an efficient framework is presented for reconstructing functional connectivity using experimental spike-train data. A modified generalized linear model (GLM) with(More)
As the amount of experimental data made publicly accessible has gradually increased in recent years, it is now possible to reconsider many of the longstanding questions in neuroscience. In this paper, we present an efficient frame-work for reconstructing the functional connectivity from the spike train data curated from the Collaborative Research in(More)
In this letter, a Hierarchical Parametric Empirical Bayes (HPEB) model is proposed to fit spike count data. We have integrated Generalized Linear Models and empirical Bayes theory to simultaneously solve three problems: (1) over-dispersion of spike count values; (2) biased estimation of the maximum likelihood method and (3) difficulty in sampling from(More)
  • Qi She, Beth Jelfs, Rosa H.M.Chan
  • 2016
In this letter, a Hierarchical Parametric Empirical Bayes model is proposed to model spike count data. We have integrated Generalized Linear Models (GLMs) and empirical Bayes theory to simultaneously provide three advantages: (1) a model of over-dispersion of spike count values; (2) reduced MSE in estimation when compared to using the maximum likelihood(More)
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