On the Synthesis of Brain-State-in-a-Box Neural Models with Application to Associative Memory

摘要

In this article we present techniques for designing associative memories to be implemented by a class of synchronous discrete-time neural net- works based on a generalization of the brain-state-in-a-box neural model. First, we address the local qualitative properties and global qualitative Aspects of the class of neural networks considered. Our approach to the Stability analysis of the equilibrium points of the network gives insight Into the extent of the domain of attraction for the patterns to be stored as Asymptotically stable equilibrium points and is useful in the analysis of The retrieval performance of the network and also for design purposes.