Modeling the Insect Mushroom Bodies: application to sequence learning Paolo Arena a,b , Marco Cal´ ı a , Luca Patan´ e a , Agnese Portera a , Roland Strauss c a Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Italy b National Institute of Biostructures and Biosystems (INBB), Viale delle Medaglie d’Oro 305, 00136 Rome, Italy c Institut f ¨ ur Zoologie III (Neurobiologie), University of Mainz, Germany Abstract Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behavior repertoire to environmental constraints. This pa- per is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial center for multimodal sensory integration and behaviour modulation. The sequence learn- ing capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classifica- tion and others. This is a clear example that a unique neural structure is able to cope concurrently with a plethora of behaviours. Simulation results and robotic experiments are reported and discussed. Key words: neuroscience, insect brain, insect mushroom bodies, spiking neurons, learning, neural model, context Email address: lpatane@dieei.unict.it (Luca Patan´ e) Preprint submitted to Neural Networks March 23, 2015