A Boolean Hebb rule for binary associative memory design

Muezzinoglu M., Guzelis C.

IEEE TRANSACTIONS ON NEURAL NETWORKS, cilt.15, sa.1, ss.195-202, 2004 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1109/tnn.2003.820669
  • Dergi Adı: IEEE TRANSACTIONS ON NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.195-202
  • Anahtar Kelimeler: Hebb rule, maximal independent set, neurodynamics and attractor networks, APPROXIMATING MAXIMUM CLIQUE, NEURAL NETWORKS, HOPFIELD NETWORKS, CONVERGENCE, ALGORITHM, SYSTEMS, GRAPHS
  • Dokuz Eylül Üniversitesi Adresli: Hayır

Özet

A binary associative memory design procedure that gives a Hopfield network with a symmetric binary weight matrix is introduced in this paper. The proposed method is based on introducing the memory vectors as maximal independent sets to an undirected graph, which is constructed by Boolean operations analogous to the conventional Hebb rule. The parameters of the resulting network is then determined via the adjacency matrix of this graph in order to find a maximal independent set whose characteristic vector is close to the given distorted vector. We show that the method provides attractiveness for each memory vector and avoids spurious memories whenever the set of given memory vectors satisfy certain compatibility conditions, which implicitly imply sparsity. The applicability of the design method is finally investigated by a quantitative analysis of the compatibility conditions.