Аннотация
It is important to validate models of neural data using appropriate
goodness-of-fit measures. Models summarizing some response features--for
example, spike count distributions or peristimulus time histograms--can
be assessed using standard statistical tools. Measuring the fit of
a full point-process model of spike trains is more difficult. Recently,
Barbieri, Quirk, Frank, Wilson, and Brown (2001) and Brown, Barbieri,
Ventura, Kass, and Frank (2002) presented a method for rescaling
time so that if an underlying description correctly describes the
conditional intensity function of a point process, the rescaling
will convert the process into a homogeneous Poisson process. The
corresponding interevent intervals are exponential with mean 1 and
can be transformed to be uniform; tests of the uniformity of the
transformed intervals are thus tests of how well the model fits the
data. When the lengths of interevent intervals are comparable to
the length of the observation window, as can happen in common neurophysiology
paradigms using short trials, the fact that long intervals cannot
be observed (are censored) can cause the tests based on time rescaling
to reject a correct model inappropriately. This article presents
a simple adjustment to the time-rescaling method to account for interval
censoring, avoiding inappropriate rejection of acceptable models
for short-trial data. We illustrate the adjustment's effect using
both simulated data and short-trial data from monkey primary visual
cortex.
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