Contributions of the input signal and prior activation history to the discharge behaviour of rat motoneurones
The principal computational operation of neurones is the transformation
of synaptic inputs into spike train outputs.
The probability of spike occurrence in neurones is determined
by the time course and magnitude of the total current reaching
the spike initiation zone.
The features of this current that are most effective in evoking spikes
can be determined by injecting a Gaussian current waveform into a neurone
and using spike-triggered reverse correlation to calculate
the average current trajectory (ACT) preceding spikes.
The time course of this ACT (and the related first-order Wiener kernel)
provides a general description of a neurone's response to dynamic stimuli.
In many different neurones, the ACT is characterized by a shallow
hyperpolarizing trough followed by a more rapid depolarizing peak
immediately preceding the spike.
The hyperpolarizing phase is thought to reflect an enhancement of
excitability by partial removal of sodium inactivation.
Alternatively, this feature could simply reflect the fact that
interspike intervals that are longer than average can only occur when
the current is lower than average toward the end of the interspike interval.
Thus, the ACT calculated for the entire spike train displays an
attenuated version of the hyperpolarizing trough associated with the
long interspike intervals.
This alternative explanation for the characteristic shape of the ACT
implies that it depends upon the time since the previous spike,
i.e. the ACT reflects both previous stimulus history and previous
The present study presents results based on recordings of noise-driven
discharge in rat hypoglossal motoneurones that support
this alternative explanation.
First, we show that the hyperpolarizing trough is larger in ACTs
calculated from spikes preceded by long interspike intervals,
and minimal or absent in those based on short interspike intervals.
Second, we show that the trough is present for ACTs calculated from
the discharge of a threshold-crossing neurone model with a postspike
after hyperpolarization (AHP), but absent from those calculated from the
discharge of a model without an AHP.
We show that it is possible to represent noise-driven discharge using
a two-component linear model that predicts discharge probability based on
the sum of a feedback kernel and a stimulus kernel.
The feedback kernel reflects the influence of prior discharge
mediated by the AHP, and it increases in amplitude when AHP
amplitude is increased by pharmacological manipulations.
Finally, we show that the predictions of this model are virtually
identical to those based on the first-order Wiener kernel.
This suggests that the Wiener kernels derived from standard white-noise
analysis of noise-driven discharge in neurones actually reflect the
effects of both stimulus and discharge history.