Neural Comput - Computing confidence intervals for point process models.

Tópicos

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Resumo

Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.

Resumo Limpo

character neural spike activ function intrins extrins factor import neurosci point process model valuabl captur inform howev process fulli appli model alway obvious complet model applic four broad step specif model estim model paramet given observ data verif model use good fit character model use confid bound step first three appli wide literatur suggest need dedic discuss timeresc theorem combin parametr bootstrap sampl can general use comput confid bound point process model first exampl use general linear model spike propens demonstr confid bound deriv bootstrap simul consist comput closedform analyt solut second exampl consid adapt point process model hippocamp place field plastic analyt confid bound can deriv demonstr simul bootstrap sampl adapt point process model use sampl generat confid bound statist test hypothesi neural represent two time point signific differ exampl design use guid perform scientif infer base point process model

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