Multistable learning dynamics in second-order neural networks with time-varying delays.

In this paper, we present new results on multistability and attractivity of second-order networks with unsupervised Hebbian-type learning component and time-varying delays. By using the properties of activation functions, we divide state space into invariant sets and establish new criteria of coexistence of equilibrium points which are exponentially stable. The attained results show that second-order synaptic interactions and learning behaviour have an important effect on the multistable convergence of the networks. Finally, numerical simulations will illustrate multistable learning dynamics of second-order networks. [ABSTRACT FROM AUTHOR]