Abstract
Synchronization of chaotic low-dimensional systems has been a topic of much recent research. Such systems have found applications for secure communications. In this work we show how synchronization can be achieved in a high-dimensional chaotic neural network. The network used in our studies is an extension of the Hopfield Network, known as the Complex Hopfield Network (CHN). The CHN, also an associative memory, has both fixed point and limit cycle or oscillatory behavior. In the oscillatory mode, the network wanders chaotically from one stored pattern to another. We show how a pair of identical high-dimensional CHNs can be synchronized by communicating only a subset of state vector components. The synchronizability of such a system is characterized through simulations.
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