Unsupervised learning and recall of temporal sequences: an application to robotics.
This paper describes an unsupervised neural network model for learning and recall of temporal patterns. The model comprises two groups of synaptic weights, named competitive feedforward and Hebbian feedback, which are responsible for encoding the static and temporal features of the sequence respectively. Three additional mechanisms allow the network to deal with complex sequences: context units, a neuron commitment equation, and redundancy in the representation of sequence states. The proposed network encodes a set of robot trajectories which may contain states in common, and retrieves them accurately in the correct order. Further tests evaluate the fault-tolerance and noise sensitivity of the proposed model.
Barretto
G A
GA
Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil. GBARRETO@SEL.EESC.SC.USP.BR
Araújo
A F
AF
D000465:Algorithms; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D012371:Robotics