Blog
About

5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      High storage capacity in the Hopfield model with auto-interactions - stability analysis

      Preprint

      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Recent studies point to the potential storage of a large number of patterns in the celebrated Hopfield associative memory model, well beyond the limits obtained previously. We investigate the properties of new fixed points to discover that they exhibit instabilities for small perturbations and are therefore of limited value as associative memories. Moreover, a large deviations approach also shows that errors introduced to the original patterns induce additional errors and increased corruption with respect to the stored patterns.

          Related collections

          Most cited references 3

          • Record: found
          • Abstract: found
          • Article: not found

          Neural networks and physical systems with emergent collective computational abilities.

           John Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Spin-glass models of neural networks

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              ‘Unlearning’ has a stabilizing effect in collective memories

                Bookmark

                Author and article information

                Journal
                25 April 2017
                Article
                10.1088/1751-8121/aa8fd7
                1704.07741

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                cond-mat.dis-nn

                Comments

                Comment on this article