title: Chained learning architectures in a simple closed-loop behavioural context.
abstract: OBJECTIVE
  Living creatures can learn or improve their behaviour by temporally correlating sensor cues where near-senses (e.g., touch, taste) follow after far-senses (vision, smell). Such type of learning is related to classical and/or operant conditioning. Algorithmically all these approaches are very simple and consist of single learning unit. The current study is trying to solve this problem focusing on chained learning architectures in a simple closed-loop behavioural context.
  
  
  METHODS
  We applied temporal sequence learning (Porr B and Wörgötter F 2006) in a closed-loop behavioural system where a driving robot learns to follow a line. Here for the first time we introduced two types of chained learning architectures named linear chain and honeycomb chain. We analyzed such architectures in an open and closed-loop context and compared them to the simple learning unit.
  
  
  CONCLUSIONS
  By implementing two types of simple chained learning architectures we have demonstrated that stable behaviour can also be obtained in such architectures. Results also suggest that chained architectures can be employed and better behavioural performance can be obtained compared to simple architectures in cases where we have sparse inputs in time and learning normally fails because of weak correlations.
authors.affiliation: Bernstein Centre for Computational Neuroscience, University of Göttingen, Bunsenstr. 10, 37073 Göttingen, Germany. tomas@bccn-goettingen.de
authors.forename: Tomas
authors.initials: T
authors.lastname: Kulvicius
authors.forename: Bernd
authors.initials: B
authors.lastname: Porr
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001519:Behavior / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics / Q000379:methods