  Nerve transmission delay is an important topic in neuroscience. Spike signals
fired or received at the dendrites of a neuron travel from the axon to the
presynaptic cell. The spike signal triggers a chemical reaction at the synapse,
wherein a presynaptic cell transfers neurotransmitters to the postsynaptic
cell, and regenerates electrical signals by a chemical reaction process through
ion channels and transmits it to neighboring neurons. In the context of
describing the complex physiological reaction process as a stochastic process,
this study aimed to show that the distribution of the maximum time interval of
spike signals follows extreme order statistics. By considering the statistical
variance in the time constant of the Leaky Integrate-and-Fire model, which is a
deterministic time evolution model of spike signals, we enabled randomness in
the time interval of spike signals. When the time constant follows an
exponential distribution function, the time interval of the spike signal also
follows an exponential distribution. In this case, our theory and simulations
confirmed that the histogram of the maximum time interval follows the Gumbel
distribution, which is one of the three types of extreme value statistics. We
also confirmed that the histogram of the maximum time interval follows a
Fr\'{e}chet distribution when the time interval of the spike signal follows a
Pareto distribution. These findings confirm that nerve transmission delay can
be described using extreme value statistics and could, therefore, be used as a
new indicator for transmission delay.
