We apply our model to survey the spiking irregularity across cortical areas and find that Poisson irregularity is a rare exception, not a rule. Our results show the need to include non-Poisson spiking in inferring neural dynamics from single trials.

In our model, a single parameter phi controls spiking irregularity, and the inhomogeneous Poisson process is just a special case of phi=1. Using intracellular voltage recordings and a spiking network model, we link this abstract statistical model to underlying biophysical processes.

We introduce a doubly stochastic renewal model that accounts for variable firing rates on single trials and can generate any spiking irregularity from periodic to super-Poisson.

However, neuronal spiking in various brain regions often diverges from Poisson irregularity, posing challenges for accurate identification of single-trial firing rate dynamics. Yet, robust methods to partition the firing rate and spiking irregularity are missing.

The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. Most methods rely on an inhomogeneous Poisson process as the standard model for partitioning firing rate and spiking irregularity.