A Boolean Hebb rule for binary associative memory design

Muezzinoglu M., Guzelis C.

IEEE TRANSACTIONS ON NEURAL NETWORKS, vol.15, no.1, pp.195-202, 2004 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 1
  • Publication Date: 2004
  • Doi Number: 10.1109/tnn.2003.820669
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.195-202
  • Keywords: Hebb rule, maximal independent set, neurodynamics and attractor networks, APPROXIMATING MAXIMUM CLIQUE, NEURAL NETWORKS, HOPFIELD NETWORKS, CONVERGENCE, ALGORITHM, SYSTEMS, GRAPHS
  • Dokuz Eylül University Affiliated: No


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.