The Hebb Rule: Storing Static and Dynamic Objects in an Associative Neural Network

The Hebb rule (Hebb, 1949) indicates how information presented to a neural network during a learning session is stored in the synapses, local elements which act as mediators between neurons. In this paper we demonstrate that the Hebb rule can be used to handle both stationary and dynamic objects such as single patterns and cycles. The two main ideas are: a) a broad distribution of delays as they occur in the natural dynamics and b) incorporation of the very same delays during the learning session. Our work shows that the resulting procedure is robust and faithful.