Spatio-Temporal Self-Organizing Feature Maps

Thus far, the success of capturing and classifying temporal information with neural networks has been limited. Our methodology adds a spatio-temporal coupling to the Self-Organized Feature Map (SOFM) which creates temporally and spatially localized neighborhoods in the map. The spatio-temporal coupling is based on traveling waves of activity starting at each winning node which are naturally attenuated over time. When these traveling waves reinforce each other, temporal activity wavefronts are created which are then used to enhance a node's possibility of winning the next competition. The spatio-temporal coupling is easily implemented with only local connectivity and calculations. Once trained, the spatio-temporal SOFM can be used for detection or for partial pattern recall. The methodology gracefully handles time-warping and multiple patterns with overlapping input vectors.