the final part consists of four chapters: two using information theory techniques to model the hippocampus and associated systems; one on the phenomena of stochastic resonance in neronal models; and one exploring the idea that cortical maps can be understood within the information maximisation framework.

The simple “hippocampus as memory system” metaphor has led the hippocampus to be one of the most modelled areas of the brain. These models extend from the very abstract (the hidden layer of a back-propagation network labelled “hippocampus”), to simulations where almost every known detail of the anatomy and physiology is incorporated. The first of the two chapters on the hippocampus modelling (Chapter 14) starts with a model of intermediate complexity. The model is simple enough to allow analytic results, whilst it is rich enough to allow the parameters to be related to known anatomy and physiology. In particular, Schultz et al.'s framework allows an investigation of the effects of changing the topography of connections between CA3 and CA1 (two subparts of the hippocampus), and different forms of representation in CA3 (binary or graded activity).

The second chapter uses similar methods, but this time working on a model that includes more of the hippocampal circuitry: specifically the entorhinal cortex and the associated perforant pathways. This inevitably introduces more parameters and makes the model more complex, but by use of methods to reduce the dimensionality of the saddle-point equations, numerical results have been obtained.

Stochastic resonance is the initially counterintuitive phenomenon that the signal-to-noise ratio (and hence information transmission) of a non-linear system can sometimes be improved by the addition of noise.