Introduction

Recent advances in techniques for the formal analysis of neural networks (Amit et al., 1987; Gardner, 1988; Tsodyks and Feigelman, 1988; Treves, 1990; Nadal and Parga, 1993) have introduced the possibility of detailed quantitative analyses of real brain circuitry. This approach is particularly appropriate for regions such as the hippocampus, which show distinct structure and for which the microanatomy is relatively simple and well known.

The hippocampus, as archicortex, is thought to predate phylogenetically the more complex neocortex, and certainly possesses a simplified version of the six-layered neocortical stratification. It is not of interest merely because of its simplicity, however: evidence from numerous experimental paradigms and species points to a prominent role in the formation of long-term memory, one of the core problems of cognitive neuroscience (Scoville and Milner, 1957; McNaughton and Morris, 1987; Weiskrantz, 1987; Rolls, 1991; Gaffan, 1992; Cohen and Eichenbaum, 1993). Much useful research in neurophysiology and neuropsychology has been directed qualitatively, and even merely categorially, at understanding hippocampal function. Awareness has dawned, however, that the analysis of quantitative aspects of hippocampal organisation is essential to an understanding of why evolutionary pressures have resulted in the mammalian hippocampal system being the way it is (Stephan, 1983; Amaral et al., 1990; Witter and Groenewegen, 1992; Treves et al., 1996). Such an understanding will require a theoretical framework (or formalism) that is sufficiently powerful to yield quantitative expressions for meaningful parameters, that can be considered valid for the real hippocampus, is parsimonious with known physiology, and is simple enough to avoid being swamped by details that might obscure phenomena of real interest.