IMPROVED PERFORMANCE OF THE HOPFIELD AND LITTLE NEURAL NETWORK MODELS WITH TIME DELAYED DYNAMICS

Abstract

We report the results of simulation of neural network models with the synaptic connections constructed using the Hebb’s rule and the dynamics determined by the internal field, which has a weighted contribution from the time delayed signals. We consider both the asynchronous (or Glauber; Hopfield) and synchronous (Little) dynamics. Our numerical results and the finite size variation study (for sizes N within the range 250 ≤N≤4000) support the previous indication [Sen and Chakrabarti, Phys. Lett.A162, 327 (1992)] of improved performance in the recall and overlap properties in the thermodynamic limit. It is identified that the time delayed term in the dynamics allows the network to come out of the spurious valleys in the “energy landscape” (defined without the delay term; Hopfield model). In an approximate analytical study of such models in the extreme dilution limit, the role of the time delayed term to suppress the (spin glass-like) noise is also indicated.