1998/07/07 by Francesco P. Battaglia, Alessandro Treves · 1 citation
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #Memory and Neural Mechanisms #Neural dynamics and brain function #Neuroscience and Neuropharmacology Research #cond-mat.dis-nn #q-bio
paper · pdf · doi:10.1103/physreve.58.7738
19 RevTeX pages, 8 pes figures
arxiv created 1998/07/07 · openalex publication_date 1998/12/01 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 autoassociative 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).