Learning temporal patterns in recurrent neural networks

General learning algorithms for recurrent neural networks that can be used for both discrete-time and continuous-time models are described. They are based on the notion of the derivatives of mappings between functions of time. Simulation results for learning rhythmical sequences are shown. The method can also be applied to networks of higher-order neuron models and multiple time delay connections.<
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