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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. 

Abstract Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here we examine this issue in the locust olfactory system. In the antennal lobe, we find that both spatial and temporal features of odor-evoked responses vary in a stimulus-history dependent manner. The response variations are not random, but allow the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity is conf ounded due to this contrast enhancement computation. Notably, predictions from a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons match results from behavioral experiments. In sum, our results suggest that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic.