Some of the (comparatively older) numerical results on neural network models obtained by our group are reviewed. These models incorporate synaptic connections constructed by using the Hebb's rule. The dynamics is determined by the internal field which has a weighted contribution from the time delayed signals. Studies on relaxation and the growth of correlations in the Hopfield model are discussed here. The memory capacity of such networks have been investigated also for some asymmetric synaptic interactions. In some cases both the asynchronous (or Glauber; Hopfield) and synchronous (Little) dynamics are used. At the end, in the appendix, we discuss the effects of asymmetric interactions on the statistical properties in a related model of spin glass (new results).
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AFFILIATION: Centre for Applied Mathematics and Computational Science, Saha Institute of Nuclear Physics, Calcutta 700064, India; Theoretical Condensed Matter Physics Division, Saha Institute of Nuclear Physics, Calcutta 700064, India.
