title: Interpolation and extrapolation in human behavior and neural networks.
abstract: Unlike most artificial systems, the brain is able to face situations that it has not learned or even encountered before. This ability is not in general echoed by the properties of most neural networks. Here, we show that neural computation based on least-square error learning between populations of intensity-coded neurons can explain interpolation and extrapolation capacities of the nervous system in sensorimotor and cognitive tasks. We present simulations for function learning experiments, auditory-visual behavior, and visuomotor transformations. The results suggest that induction in human behavior, be it sensorimotor or cognitive, could arise from a common neural associative mechanism.
authors.affiliation: INSERM U483, Universitacuté Pierre et Marie Curie, Paris, France. guigon@ccr.jussieu.fr
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Guigon
mesh_terms: D000223:Adaptation, Psychological; D001185:Artificial Intelligence; D001519:Behavior / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011597:Psychomotor Performance / Q000502:physiology