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Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. 

Understanding olfactory processing in insects requires characterizing the complex dynamics and connectivity of the first olfactory relay - antennal lobe (AL). We leverage in vivo electrophysiology to train recurrent neural network (RNN) model of the locust AL, inferring the underlying connectivity and temporal dynamics. The RNN comprises 830 projection neurons (PNs) and 300 local neurons (LNs), replicating the locust AL anatomy. The trained network reveals sparse connectivity, with different connection densities between LNs and PNs and no PN-PN connections, consistent with in vivo data. The learned time constants predict slower LN dynamics and diverse PN response patterns, with low and high time constants correlating with early and late odor-evoked activity, as reported in vivo. Our approach demonstrates the utility of biologically-constrained RNNs in inferring circuit properties from empirical data, providing insights into mechanisms of odor coding in the AL.