title: A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.
abstract: Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.
authors.affiliation: * Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy.|† National Institute of Biostructures and Biosystems (INBB), Viale delle Medaglie d'Oro 305, 00136 Rome, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Arena
authors.affiliation: * Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Calí
authors.affiliation: * Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Patané
authors.affiliation: * Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale A. Doria 6, Catania, 95100, Italy.
authors.forename: Agnese
authors.initials: A
authors.lastname: Portera
authors.affiliation: ǁ Institut für Zoologie III (Neurobiologie), University of Mainz, Mainz, Germany.
authors.forename: Roland
authors.initials: R
authors.lastname: Strauss
mesh_terms: D000200:Action Potentials; D000818:Animals; D003198:Computer Simulation; D003657:Decision Making; D004331:Drosophila melanogaster / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology*; D024521:Mushroom Bodies / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010465:Perception / Q000502:physiology*; D012201:Reward; D012371:Robotics
keywords: Insect mushroom bodies; bio-inspired control; learning; neural model; resonant neurons; spiking neurons