Populations of neurons often respond in a redundant fashion to stimuli. One such redundancy is that multiple neurons react to similar stimulus features, and another is that neurons excite or inhibit each other. Using information theoretic principles, redundancy measures are defined and analyzed for a series of theoretical models in this thesis. Furthermore, the redundancy measures are applied to measurement data from populations of neurons in the auditory system of Zebra Finch Song Birds. The data suggests that the amount of redundancy varies from one population to another. This thesis attempts to understand and to distinguish between neural populations based on their relative amounts of redundancies. Finally, a coarse approach to characterizing redundancy is developed via considering the mutual information between the stimulus and the responses of one, two, three etc. neurons in the considered population. This coarse characterization is analyzed for several theoretical models. The results show that a rapid increase in information with the number of neurons in the population suggests high redundancy between neurons while a slow increase implies low redundancy.