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 Networks (ANN) models, neurons in 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. RNN has been applied in a variety of areas including pattern recognition, classification, image processing, combinatorial optimisation 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.
The RNN has been applied in a variety of areas including pattern recognition, classification, image processing, and combinatorial optimization and communication systems REF .
The Random Neural Network: A Survey
Comput. J.
Comput. J.
Computer Science