Partial reset is a simple and powerful tool for controlling the irregularity of spike trains fired by a leaky integrator neuron model with random inputs. In particular, a single neuron model with a realistic membrane time constant of 10 ms can reproduce the highly irregular firing of cortical neurons reported by Softky and Koch (1993). In this article, the mechanisms by which partial reset affects the firing pattern are investigated. Itisshown theoretically that partial reset is equivalent to the use of a time-dependent threshold, similar to a technique proposed by Wilbur and Rinzel (1983) to produce high irregularity. This equivalent model allows establishing that temporal integration and fluctuation detection can coexist and cooperate to cause highly irregular firing. This study also reveals that reverse correlation curves cannot be used reliably to assess the causes of firing. For instance, they do not reveal temporal integration when it takes place. Further, the peak near time zero does not always indicate coincidence detection. An alternative qualitative method is proposed here for that later purpose. Finally, it is noted that as the reset becomes weaker, the firing pattern shows a progressive transition from regular firing, to random, to temporally clustered, and eventually to bursting firing. Concurrently the slope of the transfer function increases. Thus, simulations suggest a correlation between high gain and highly irregular firing.