An instance of coincidence detection architecture relying on temporal coding.
Although time and space are interrelated in every occurrence of real-world events, only spatial codes are used at the basic level of most computational architectures. Inspired by neurobiological facts and hypotheses that assign a primordial coding role to the temporal dimension, and developed to address both cognitive and engineering applications, guided propagation networks (GPNs) are aimed at a generic real-time machine, based on time-space coincidence testing. The involved temporal parameters are gradually introduced, in relation with complementary applications in the field of human-machine communication: sensori-motor modeling, pattern recognition and natural language processing.
Béroule
Dominique
D
Architectures and Models for Interaction Group, Laboratoire d'informatique et de Mécanique pour les Sciences de l'ngénieur-Centre National de la Recherche Scientifique, 91403 Orsay, France. domi@limsi.fr
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D002490:Central Nervous System / Q000502:physiology; D006801:Humans; D007802:Language; D008959:Models, Neurological; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology; D012677:Sensation / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology*; D013998:Time Perception / Q000502:physiology*; D014584:User-Computer Interface; D014705:Verbal Behavior / Q000502:physiology