Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset

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 paper, the mechanisms by which partial reset affects the firing pattern are investigated. It is shown theoretically that partial reset is equivalent to the use of a time dependent threshold, similarly to a technique proposed by Wilbur and Rinzel, (1983) to produce high irregularity. This equivalent model allows to establish that temporal integration and fluctuation detection can coexist and co-operate to cause highly irregular firing. This study also revealed that reverse correlation curves can not reliably be used to assess the causes of firing. For instance they did not reveal temporal integration when it took place. Further, the peak near time zero did not always indicate coincidence detection. An alternative qualitative method is proposed here for that 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.