Modelling the insect Mushroom Bodies: Application to sequence learning

Learning and reproducing temporal sequences is a fundamental ability used by
living beings to adapt behavior repertoire to environmental constraints. This paper 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 learning capability coexists, within the insect brain computational model, with all the
other features already addressed like attention, expectation, learning classification 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