  Research showed that, the information transmitted in biological neurons is
encoded in the instants of successive action potentials or their firing rate.
In addition to that, in-vivo operation of the neuron makes measurement
difficult and thus continuous data collection is restricted. Due to those
reasons, classical mean square estimation techniques that are frequently used
in neural network training is very difficult to apply. In such situations,
point processes and related likelihood methods may be beneficial. In this
study, we will present how one can apply certain methods to use the
stimulus-response data obtained from a neural process in the mathematical
modeling of a neuron. The study is theoretical in nature and it will be
supported by simulations. In addition it will be compared to a similar study
performed on the same network model.
