Self-modeling in Hopfield Neural Networks with Continuous Activation Function

Hopfield networks can exhibit many different attractors of which most are local optima. It has been demonstrated that combining states randomization and Hebbian learning enlarges the basin of attraction of globally optimal attractors. The procedure is called self-modeling and it has been applied in symmetric Hopfield networks with discrete states and without self-recurrent connections. We are interested in knowing which topological constraints can be relaxed. So, the self-modeling process is tested in asymmetric Hopfield networks with continuous states and self-recurrent connections. The best results are obtained in networks with modular structure.