The random neural network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most artificial neural network models, neurons in the RNN interact by probabilistically exchanging excitatory and inhibitory spiking signals. The model is described by analytical equations, has a low complexity supervised learning algorithm and is a universal approximator for bounded continuous functions. The RNN has been applied in a variety of areas including pattern recognition, classification, image processing, combinatorial optimization and communication systems. It has also inspired research activity in modelling interacting entities in various systems such as queueing and gene regulatory networks. This paper presents a review of the theory, extension models, learning algorithms and applications of the RNN.
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