  The model of the current paper is an extension of a previous publication,
wherein we used the leaky integrate-and-fire model on a regular lattice with
periodic boundary conditions, and introduced the temporal complexity as a
genuine signature of criticality. In that work, the power-law distribution of
neural avalanches was manifestation of supercriticality rather than
criticality. Here, however, we show that continuous solution of the model and
replacing the stochastic noise with a Gaussian zero-mean noise leads to the
coincidence of power-law display of temporal complexity and spatiotemporal
patterns of neural avalanches at the critical point. We conclude that the
source of inconsistency may in fact be a numerical artifact originated by the
discrete description of the model, which may imply slow numerical convergence
of avalanche distribution compared to temporal complexity.
