Learning dynamics in second order networks

A model of a second order neural network incorporating a weight adjusting learning component and time delays in processing and transmission is formulated. Delay-independent sufficient conditions are derived for the existence of an asymptotically and exponentially stable equilibrium state. The learning dynamics is modelled with an unsupervised Hebbian-type learning algorithm together with a forgetting term. If there is nothing for the network to learn or if there are no second order synaptic interactions, then our analysis will correspond to one of the standard model of neural networks.