The linear-nonlinear cascade model (LN model) has
proven very useful in representing a neural system?s encoding
properties, but has proven less successful in reproducing the
firing patterns of individual neurons whose behavior is strongly
dependent on prior firing history. While the cell?s behavior can
still usefully be considered as feature detection acting on a
fluctuating input, some of the coding capacity of the cell is taken
up by the increased firing rate due to a constant ?driving? direct
current (DC) stimulus. Furthermore, both the DC input and the
post-spike refractory period generate regular firing, reducing the
spike-timing entropy available for encoding time-varying fluctuations.
In this paper, we address these issues, focusing on the
example of motoneurons in which an afterhyperpolarization
(AHP) current plays a dominant role regularizing firing behavior.
We explore the accuracy and generalizability of several alternative
models for single neurons under changes in DC and variance
of the stimulus input. We use a motoneuron simulation to compare
coding models in neurons with and without the AHP
current. Finally, we quantify the tradeoff between instantaneously
encoding information about fluctuations and about the DC