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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 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.

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