  In this paper, we study analytically the impact of an inhibitory autapse on
neuronal activity. In order to do this, we formulate conditions on a set of
non-adaptive spiking neuron models with delayed feedback inhibition, instead of
considering a particular neuronal model. The neuron is stimulated with a
stochastic point renewal process of excitatory impulses. Probability density
function (PDF) $p(t)$ of output interspike intervals (ISIs) of such a neuron is
found exactly without any approximations made. It is expressed in terms of ISIs
PDF for the input renewal stream and ISIs PDF for that same neuron without any
feedback. Obtained results are applied to a subset of neuronal models with
threshold 2 when the time intervals between input impulses are distributed
according to the Erlang-2 distribution. In that case we have found explicitly
the model-independent initial part of ISIs PDF $p(t)$ defined at some initial
interval $[0;T_2]$ of ISI values.
