the first part concentrates on how information theory can give us insight into low-level vision, an area that has many characteristics that make it particularly appropriate for the application of such techniques. Chapter 2, by Burton, is a historical review of the application of information theory to understanding the retina and early cortical areas. The rather impressive matches of a number of models to data are described, together with the different emphases placed by researchers on dealing with noise, removing correlations, and having representations that are amenable to later processing.

Information theory only really works if information transmission is maximised subject to some constraint. In Chapter 3 Laughlin et al. explore the explanatory power of considering one very important constraint: the use of energy. This is conceptually very neat, since there is a universal biological unit of currency, the ATP molecule, allowing the costs of various neuronal transduction processes to be related to other important costs to an insect, such as the amount of energy required to fly. There are a wealth of ideas here and the insect is an ideal animal to explore them, given our good knowledge of physiology, and the relative simplicity of collecting the large amounts of physiological data required to estimate the statistics required for information theoretical descriptions.

To apply the concepts of information theory, at a very minimum one needs a reasonable model of the input statistics. In vision, the de facto model is based on the fact that the power spectra of natural images have a structure where the power at a given frequency is proportional to one over that frequency squared.