  We wish to discriminate spike sequences based on the degree of irregularity.
For this purpose, we search for a rational expressions of quadratic functions
of consecutive interspike intervals that efficiently measures spiking
irregularity. Under natural assumptions, the functional form of the coefficient
can be parameterized by a single parameter. The parameter is determined so as
to maximize the mutual information between the distributions of coefficients
computed for spike sequences derived from different renewal point processes. We
find that the local variation of interspike intervals, LV (Neural Comput. Vol.
15, pp. 2823-42, 2003), is nearly optimal for whose intrinsic irregularity is
close to that of experimental data.
