A recurrent neural network (RNN) is a class of neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior.
Recurrent Neural Network A network similar to a feedforward neural network except that there may be connections from an output or hidden layer to the inputs. Recurrent neural networks are capable of universal computation.
• Recurrent neural networks trained using sequential-state estimation algorithms.
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Recurrent neural network (finite-precision inputs/outputs/weights, infinite-precision signals initialized to zero)
Cellular automaton, including: ...
One of the earliest recurrent neural networks reported in literature was the auto-associator independently described by Anderson (Anderson, 1977) and Kohonen (Kohonen, 1977) in 1977.
* "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference" by Steve Lawrence, Ah Chung Tsoi and C. Lee Giles is available from NEC in New Jersey.
The hidden units of a conventional recurrent neural network have now been replaced by memory blocks, each of which contains one or more memory cells.
Silipo and Marchesi use static and recurrent neural network (RNN) architectures for the classification tasks in ECG analysis for arrhythmia, myocardial ischemia and chronic alterations.
The Hopfield net is a recurrent neural network in which all connections are symmetric. This network has the property that its dynamics are guaranteed to converge.
The information flows straight from the inputs to the output, with no feedback allowed as in recurrent neural networks. This permits an efficient simulation.
 See also: Neural network, Classification, Percept, Hopfield net, Perceptron
  
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