A spiking neural network model of the locust antennal lobe: Towards neuromorphic electronic noses inspired from insect olfaction

In analogy with the non-selectivity of gas sensors, an olfactory receptor neuron is not tuned to a specific odor and hence presents a lack of selectivity. Despite this shortcoming, insects have impressive abilities to recognize odors. Understanding how their olfactory system works could then be highly beneficial for designing efficient electronic noses. In particular, the antennal lobe, the first structure of the insect olfactory system, is known to encode odors by spatio-temporal patterns of activation of projection neurons. We propose here a simplified, but still biologically plausible, model of the locust antennal lobe. Our model is a network of single variable spiking neurons coupled via simple exponential synapses. Its reduced complexity allows a deeper understanding of the mechanisms responsible of the network oscillatory behavior and of the spatio-temporal coding of the stimulus. In particular, we show how a stimulus is robustly encoded at each oscillation of the network by a spatial assembly of quasi-synchronized projection neurons, each one being individually phase-locked to the local field potential. Moreover, it is shown that frequency adaptation is responsible of the temporal evolution of this spatial code and that this temporal aspect of the code is crucial in enhancing the distance between the representations of similar odors.