A New Neural Network Model For Temporal Processing
We present a novel neural network model for real-time processing of space-time patterns. The model is characterized by high order temporal dynamics, a feature that adds computational flexibility in the time domain compared to common neural models. Another important property is that although our model uses temporal synaptic weights (w = w(t)), the system equations can be written as a regular additive (Grossberg) model. This is advantageous, because a substantial body of learning and stability theory frr the additive model is directly applicable to our model. Applications include real-time classification of speech or EEG data and nonlinear prediction.