Block information and topology in memory networks
David Domínguez · AIP conference proceedings · 2007
The retrieval abilities of spatially uniform attractor networks can be measured by the average overlap between patterns and neural states. Metric networks (with local connections), like small‐world graphs, modelled by the parameters: connectivity γ and randomness ω, however, display a richer distribution of memory attractors. We found that metric networks can carry information structured in blocks without any global overlap. There is a competition between global and blocks attractors. We propose a way to measure the block information, related to the fluctuations of the overlap over the blocks. The phase‐diagram with the transition from local to global information, shows that the stability of blocks grows with dilution, but decreases with the storage rate and disappears for random topologies.