Contibutions of input and history to motoneuron output (Powers et al 2005)

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Accession:83508
"The present study presents results based on recordings of noise-driven discharge in rat hypoglossal motoneurones ... First, we show that the hyperpolarizing trough is larger in Average Current Trajectories (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 kernel derived from standard white-noise analysis of noise-driven discharge in neurones actually reflect the effects of both stimulus and discharge history." See paper for more and details.
Reference:
1 . Powers RK, Dai Y, Bell BM, Percival DB, Binder MD (2005) Contributions of the input signal and prior activation history to the discharge behaviour of rat motoneurones. J Physiol 562:707-24 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
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Simulation Environment: IGOR Pro;
Model Concept(s): Simplified Models;
Implementer(s): Powers, Randy [rkpowers at u.washington.edu];
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Powers RK, Dai Y, Bell BM, Percival DB, Binder MD (2005)
Contributions of the input signal and prior activation history to the
discharge behaviour of rat motoneurones.
J Physiol 562:707-24

Abstract: 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 discharge history. 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
afterhyperpolarization (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.

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