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Large-scale distributed systems, such as natural neuronal and artificial systems, have many local interconnections, but they often also have the ability to propagate information very fast over relatively large distances. Mechanisms that enable such behavior include very long physical signaling paths and possibly saccades of synchronous behavior that may propagate across a network. This letter studies the modeling of such behaviors in neuronal networks and develops a related learning algorithm. This is done in the context of the random neural network (RNN), a probabilistic model with a well-developed mathematical theory, which was inspired by the apparently stochastic spiking behavior of certain natural neuronal systems. Thus,we develop an extension of the RNN to the case when synchronous interactions can occur, leading to synchronous firing by large ensembles of cells. We also present an O(N3) gradient descent learning algorithm for an N-cell recurrent network having both conventional excitatory-inhibitory interactions and synchronous interactions. Finally, the model and its learning algorithm are applied to a resource allocation problem that is NP-hard and requires fast approximate decisions. [ABSTRACT FROM AUTHOR]

Introduces a learning algorithm which applied both to recurrent and feedforward multiple signal class random neural networks (MCRNN). Popularity of artificial neural networks; Mathematical model of the MCRNN; Formula for the learning algorithm.

We consider the effect on system performance of the distribution of a data base in the form of multiple copies at distinct sites. The purpose of our analysis is to determine the gain in READ throughput that can be obtained in the presence of consistency preserving algorithms that have to be implemented when UPDATE operations are carried out on each copy. We show that READ throughput diminishes if the number or copies exceeds an optimal value. The theoretical model we develop is applied to a system in which consistency is preserved through the use of Ellis' ring algorithm. [ABSTRACT FROM AUTHOR]