title: Structured information in small-world neural networks.
abstract: The retrieval abilities of spatially uniform attractor networks can be measured by the global overlap between patterns and neural states. However, we found that nonuniform networks, for instance, small-world networks, can retrieve fragments of patterns (blocks) without performing global retrieval. We propose a way to measure the local retrieval using a parameter that is related to the fluctuation of the block overlaps. Simulation of neural dynamics shows a competition between local and global retrieval. The phase diagram shows a transition from local retrieval to global retrieval when the storage ratio increases and the topology becomes more random. A theoretical approach confirms the simulation results and predicts that the stability of blocks can be improved by dilution.
authors.affiliation: EPS, Universidad Autónoma de Madrid, 28049 Madrid, Spain. david.dominguez@uam.es
authors.forename: David
authors.initials: D
authors.lastname: Dominguez
authors.forename: Mario
authors.initials: M
authors.lastname: González
authors.forename: Eduardo
authors.initials: E
authors.lastname: Serrano
authors.forename: Francisco B
authors.initials: FB
authors.lastname: Rodríguez
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D011939:Mental Recall / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*