Learning expectation in insects: A recurrent spiking neural model for spatio-temporal representation

Insects are becoming a reference point in Neuroscience for the study of biological aspects at the basis of cognitive processes. These animals have much simpler brains with respect to higher animals, showing, at the same time, impressive capability to adaptively react and take decision in front of complex environmental situations. In this paper we propose a neural model inspired by the insect olfactory system, with particular attention of the fruit fly Drosophila melanogaster. This architecture is a multilayer spiking network, where each layer is inspired by the structures of the insect brain mainly involved in the representation of the olfactory information, namely the Mushroom Bodies, the Lateral Horns and the Antennal Lobes. The Antennal Lobes layer is based on a competitive topology between sets of neurons. Its function is to transform the sensorial information into a pattern, projecting such information to the Mushroom Bodies layer. Here a competitive reaction-diffusion process leads to a spontaneous emerging of clusters. The Lateral Horns have been modeled as a delayed input-triggered resetting system. Using plastic recurrent connections with the addition of simple learning mechanisms, the structure is able to realize a top-down modulation at the input level. This leads to the emergence of an attentional loop as well as to the arousal of expectation based behaviors in case of subsequently presented stimuli. Simulation results and analysis on the biological plausibility of the architecture are provided and the role of noise in the network is reported.