A recurrent neural network model storing multiple spatial maps, or
``charts'', is analyzed. A network of this type has been suggested as a model
for the origin of place cells in the hippocampus of rodents. The extremely
diluted and fully connected limits are studied, and the storage capacity and
the information capacity are found. The important parameters determining the
performance of the network are the sparsity of the spatial representations and
the degree of connectivity, as found already for the storage of individual
memory patterns in the general theory of auto-associative networks. Such
results suggest a quantitative parallel between theories of hippocampal
function in different animal species, such as primates (episodic memory) and
rodents (memory for space).Comment: 19 RevTeX pages, 8 pes figure
