document: A self-organizing context-based approach to the tracking of multiple robot trajectories We have combined competitive and Hebbian learning in a neural network designed to learn and recall complex spatiotemporal sequences . In such sequences , a particular item may occur more than once or the sequence may share states with another sequence . Processing of repeated/shared states is a hard problem that occurs very often in the domain of robotics . The proposed model consists of two groups of synaptic weights : competitive interlayer and Hebbian intralayer connections , which are responsible for encoding respectively the spatial and temporal features of the input sequence . Three additional mechanisms allow the network to deal with shared states : context units , neurons disabled from learning , and redundancy used to encode sequence states . The network operates by determining the current and the next state of the learned sequences . The model is simulated over various sets of robot trajectories in order to evaluate its storage and retrieval abilities ; its sequence sampling effects ; its robustness to noise and its tolerance to fault
doc_bio_tags: O O O O O O O O O B I O O O O O B I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O B I I O O O O O O O O O O
extractive_keyphrases: hebbian learning complex spatiotemporal sequences synaptic weights hebbian intralayer connections shared states context units sequence states robot trajectories retrieval abilities sequence sampling effects
abstractive_keyphrases: self-organizing context-based approach trajectories tracking competitive learning competitive interlayer connections unsupervised learning storage abilities fault tolerance