  Neurons in the nervous system are submitted to distinct sources of noise,
such as ionic-channel and synaptic noise, which introduces variability in their
responses to repeated presentations of identical stimuli. This motivates the
use of stochastic models to describe neuronal behavior. In this work, we
characterize an intrinsically stochastic neuron model based on a
voltage-dependent spike probability function. We determine the effect of the
intrinsic noise in single neurons by measuring the spike time reliability and
study the stochastic resonance phenomenon. The model was able to show increased
reliability for non-zero intrinsic noise values, according to what is known
from the literature, and the addition of intrinsic stochasticity in it enhanced
the region in which stochastic resonance is present. We proceeded to the study
at the network level where we investigated the behavior of a random network
composed of stochastic neurons. In this case, the addition of an extra
dimension, represented by the intrinsic noise, revealed dynamic states of the
system that could not be found otherwise. Finally, we propose a method to
estimate the spike probability curve from in vitro electrophysiological data.
