  We describe a new, computationally simple method for analyzing the dynamics
of neuronal spike trains driven by external stimuli. The goal of our method is
to test the predictions of simple spike-generating models against
extracellularly recorded neuronal responses. Through a new statistic called the
power ratio, we distinguish between two broad classes of responses: (1)
responses that can be completely characterized by a variable firing rate, (for
example, modulated Poisson and gamma spike trains); and (2) responses for which
firing rate variations alone are not sufficient to characterize response
dynamics (for example, leaky integrate-and-fire spike trains as well as Poisson
spike trains with long absolute refractory periods). We show that the responses
of many visual neurons in the cat retinal ganglion, cat lateral geniculate
nucleus, and macaque primary visual cortex fall into the second class, which
implies that the pattern of spike times can carry significant information about
visual stimuli. Our results also suggest that spike trains of X-type retinal
ganglion cells, in particular, are very similar to spike trains generated by a
leaky integrate-and-fire model with additive, stimulus-independent noise that
could represent background synaptic activity.
