  At the level of individual neurons, various coding properties can be inferred
from the input-output relationship of a cell. For small inputs, this relation
is captured by the phase-response curve (PRC), which measures the effect of a
small perturbation on the timing of the subsequent spike. Experimentally,
however, an accurate experimental estimation of PRCs is challenging. Despite
elaborate measurement efforts, experimental PRC estimates often cannot be
related to those from modeling studies. In particular, experimental PRCs rarely
resemble the generic PRC expected close to spike initiation, which is
indicative of the underlying spike-onset bifurcation. Here, we show for
conductance-based model neurons that the correspondence between theoretical and
measured phase-response curve is lost when the stimuli used for the estimation
are too large. In this case, the derived phase-response curve is distorted
beyond recognition and takes on a generic shape that reflects the measurement
protocol, but not the real neuronal dynamics. We discuss how to identify
appropriate stimulus strengths for perturbation and noise-stimulation methods,
which permit to estimate PRCs that reliably reflect the spike-onset bifurcation
-- a task that is particularly difficult if a lower bound for the stimulus
amplitude is dictated by prominent intrinsic neuronal noise.
