Structured information in small-world neural networks
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
American Physical SocietyDate
2009-02-02Citation
10.1103/PhysRevE.79.021909
Physical Review E 79 (2009 ): 021909
ISSN
1539-3755 (print); 1550-2376 (online)DOI
10.1103/PhysRevE.79.021909Funded by
This work was supported by the MEC Grants No. TIN- 2004-04363-CO03-03, No. TIN-2007-65989 and by the CAM Grant No. S-SEM-0255-2006. E.S. was partially supported by the MEC Grant No. PR2007-0080. We thank K. Koroutchev and R. Levi for useful discussion.Project
Comunidad de Madrid. S2006/SEM-0255/OLFACTOSENSEEditor's Version
http://journals.aps.org/pre/pdf/10.1103/PhysRevE.79.021909Subjects
InformáticaRights
© 2009 American Physical SocietyAbstract
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.
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Google Scholar:Domínguez Carreta, David Renato
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González, Mario
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Serrano Jerez, Eduardo
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Rodríguez Ortiz, Francisco Borja
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