Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
Convergence time characteristics of an associative memory for natural language processing
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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The authors have analyzed the dynamics of associative neural networks based on macroscopic state equations and have shown that both a layered associative net and an autocorrelation type net have the same convergence property: If a recalling process succeeds, the network converges very fast to one of the memorized patterns. But if a recalling process fails, it converges very slowly to a spurious state or does not converge. This property was also checked by computer simulations on a large scale (N = 1000) neural network. Moreover, it is shown that the convergence time for a successful recall is of order log(N). If this convergence time difference is used, execution time and memory can be saved and it can be determined whether a recalling process succeeds or fails without any additional procedure.