  We present a first-order non-homogeneous Markov model for the
interspike-interval density of a continuously stimulated spiking neuron. The
model allows the conditional interspike-interval density and the stationary
interspike-interval density to be expressed as products of two separate
functions, one of which describes only the neuron characteristics, and the
other of which describes only the signal characteristics. This allows the use
of this model to predict the response when the underlying neuron model is not
known or well determined. The approximation shows particularly clearly that
signal autocorrelations and cross-correlations arise as natural features of the
interspike-interval density, and are particularly clear for small signals and
moderate noise. We show that this model simplifies the design of spiking neuron
cross-correlation systems, and describe a four-neuron mutual inhibition network
that generates a cross-correlation output for two input signals.
