An insect brain inspired neural model for object representation and expectation

In spite of their small brain, insects show a complex behavior repertoire and are becoming a reference point in neuroscience and robotics. In particular, it is very interesting to analyze how biological reaction-diffusion systems are able to codify sensorial information with the addition of learning capabilities. In this paper we propose a new model of the olfactory system of the fruit fly Drosophila melanogaster. The architecture is a multi-layer spiking neural network, inspired by the structures of the insect brain mainly involved in the olfactory conditioning, namely the Mushroom Bodies, the Lateral Horns and the Antennal Lobes. The Antennal Lobes model is based on a competitive topology that transduces the sensorial information into a pattern, projecting such information to the Mushroom Bodies model. This model is based on a first and second order reaction-diffusion paradigm that leads to a spontaneous emerging of clusters. The Lateral Horns have been modeled as an input-triggered resetting system. The structure, besides showing the already known capabilities of associative learning, via a bottom-up processing, is also able to realize a top-down modulation at the input level, in order to implement an expectation-based filtering of the sensorial inputs.