A recurrent neural network model storing multiple spatial maps, or``charts'', is analyzed. A network of this type has been suggested as a modelfor the origin of place cells in the hippocampus of rodents. The extremelydiluted and fully connected limits are studied, and the storage capacity andthe information capacity are found. The important parameters determining theperformance of the network are the sparsity of the spatial representations andthe degree of connectivity, as found already for the storage of individualmemory patterns in the general theory of auto-associative networks. Suchresults suggest a quantitative parallel between theories of hippocampalfunction in different animal species, such as primates (episodic memory) androdents (memory for space).
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