Insect inspired spatialoral cellular processing for feature-action learning

In this paper an insect brain-inspired neural processing architecture was developed to be applied on board of a bio-robot for solving feature-to-action tasks. The system, accounting on visual features, is able to solve a classification problems using a spatial temporal approach that is typical of bio-inspired neural architectures. The proposed neural structure, taking inspiration from a specific neuropile of the insect brain, called mushroom bodies, is applied to solve tasks shown in insect experiments where non-elemental learning strategies are taken into account. An important peculiarity of the hidden processing layer of the proposed multi-layer architecture is the local, CNN-like connectivity among the spiking neurons, opening the way for an hardware implementation on neuromorphic chips.