Entropy Based Delta Rule for Supervised Training
of Temporal Sequence Sensitive Neuron
Institute of Information Technologies, 1113 Sofia
A robust method for recognition of specific temporal correlations in the input pattern is presented. The method is based on a modified model of spiking neuron. A supervised single neuron training algorithm is proposed. The training rule could be used with both types of input patterns - rate-coded spatial pattern and temporal coded pattern. A Cascade-Correlation architecture enables complex temporal sequence recognition.
Keywords: spiking neuron model, entropy based Delta rule, supervised neuron training algorithm.