Learning slow features with reservoir computing for biologically-inspired robot localization

Neural Networks

Volumn 25, Issue , 2012, Pages 178-190

  Antonelo, Eric ^a   Schrauwen, Benjamin ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Independent Component Analysis; Place cells; Reservoir computing; Robot localization; Slow Feature Analysis

Indexed keywords

BIOLOGICALLY-INSPIRED ROBOTS; DISTANCE SENSORS; FADING MEMORY; HIGH-DIMENSIONAL; INPUT STREAMS; NON-LOCALIZED; ODOMETRY; PLACE CELL; REAL ROBOT; RESERVOIR COMPUTING; ROBOT ENVIRONMENT; ROBOT LOCALIZATION; SECOND LAYER; SENSOR READINGS; SLOW FEATURE ANALYSIS; SPARSE CODING; SPATIAL REPRESENTATIONS;

CHEMICAL ACTIVATION; INDEPENDENT COMPONENT ANALYSIS; RECURRENT NEURAL NETWORKS; ROBOT APPLICATIONS; SENSORS;

ROBOTS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; HIPPOCAMPUS; INDEPENDENT COMPONENT ANALYSIS; MATHEMATICAL MODEL; NOISE; PREDICTION; PRIORITY JOURNAL; RESERVOIR COMPUTING; ROBOTICS; RODENT; SENSOR; SIGNAL PROCESSING; SIMULATION; SLOW FEATURE ANALYSIS;

ANIMALS; LEARNING; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); RATS; ROBOTICS; TIME FACTORS;

EID: 82355169505     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.08.004     Document Type: Article

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