Information theory studies how communication systems integrate, encode, compute, and transmit information. The neural system is an example where communication takes place with neurons responding to stimuli and conducting electrical impulses by means of action potentials thereby causing a flow of neuronal information throughout the system. In this sense, one of the goals of this work is to study some information theory measures, with special attention to their application in the study of neural connectivity. The second goal is to understand the process of mathematical modeling of neuronal spike-trains prescribed from stochastic chains with memory of variable length as well as proposing estimators for the information metrics based on samples of these chains. Lastly, we study the performance of the estimators via computational simulation.