  Regular firing neurons can be seen as oscillators. The phase-response curve
(PRC) describes how such neurons will respond to small excitatory
perturbations. Knowledge of the PRC is important as it is associated to the
excitability type of neurons and their capability to synchronize in networks.
In this work we present a novel method to estimate the PRC from experimental
data. We assume that continuous noise signal can be discretized into
independent perturbations at evenly spaced phases and predict the next spike
based on these independent perturbations. The difference between the predicted
next spike made at every discretized phase and the actual next spike time is
used as the error signal used to optimize the PRC. We test our method on model
data and experimentally obtained data and find that the newly developed method
is robust and reliable method for the estimation of PRCs from experimental
data.
