A major challenge in neuroscience is to understand neural coding - how sensory stimuli, or motor actions controlling the behaviour of an animal, are represented by the activity of neurons in the nervous system. Information theory provides a powerful tool for investigating such codes; a rigorous non-parametric theoretical framework for quantifying the properties of noisy communication channels. By viewing various stages of the nervous system as such channels, we can use information theory to obtain meaningful quantitative results about the information capacity of the system, the sensory stimuli or features that are represented, as well as the performance of different candidate codes.In this thesis, we first develop an open source neuroinformatics toolbox implementing robust estimators and bias-corrections for a range of information theoretic quantities. This also includes a novel implementation of an algorithm for numerically obtaining distributions with maximum entropy over discrete probability spaces subject to marginal equality constraints. These maximum entropy distributions provide a powerful tool for investigating the effect of interactions on information transmission by neural population codes.These neuroinformatics tools are then used to explore population coding of the velocity of sinusoidal whisker stimulation in the cortex of anaesthetised rats. We show that both with a pooled model (assuming the neural population is homogenous) and with a labelled line model, interactions are present and affect the information transmission in the system. We show that interactions of order higher than two have a measurable but minor effect on the information capacity of the neural population. This is the first direct quantification of the effect of high order interactions on information transmission in a neural system, and is one of the first studies for which the data lie outside of the perturbative regime in which pairwise models are guaranteed to perform well.We then consider results from a novel experimental preparation, recording from populations in VPm thalamus under both white noise and naturalistic whisker stimulation. We show that sub-millisecond precise spike timing, previously observed with white noise stimuli, is also present with naturalistic stimuli, and that the diverse feature selectivity previously observed across different single unit recording sessions is also present with neurons simultaneously recorded within a single barrelloid. We use a novel information theoretic approach to probe the kinetic selectivity of the recorded cells, showing that they encode combinations of position, velocity and acceleration and that of these, velocity is the best encoded feature. We also quantify the information available to cortex under both a count code and a labelled line code, showing that a simple pooling of the population by a downstream decoder results in a large loss of information, but that this loss may be ameliorated by choosing more carefully the subpopulations over which to pool activity. Finally, we again apply the maximum entropy tools to quantify the effect of interactions, including a novel calculation of the maximal information available to a downstream decoder neglecting correlations of different orders, and find that, similar to the results in cortex, high order correlations do have a measurable effect on information transmission.