Neural networks that learn state space trajectories by `Hebbian´ rule

Summary form only given, as follows. A neural network structure is proposed which can learn state-space trajectories (sequential state transitions) by a Hebbian-like rule, but without resorting to time-delayed synaptic connections. The main idea is to use two Hopfield networks, each of which stabilizes its own memories while it drives the other network into state transition. The dynamics of the network are considered. As an emergent property, the state transitions of all individual neurons are synchronous. The learning rate of the network is estimated