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Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable. To promote an open data culture, we have therefore developed the InsectBrainDatabase (IBdb), a free online platform for insect neuroanatomical and functional data. The IBdb facilitates biological insight by enabling effective cross-species comparisons, by linking neural structure with function, and by serving as general information hub for insect neuroscience. The IBdb allows users to not only effectively locate and visualize data, but to make them widely available for easy, automated reuse via an application programming interface. A unique private mode of the database expands the IBdb functionality beyond public data deposition, additionally providing the means for managing, visualizing, and sharing of unpublished data. This dual function creates an incentive for data contribution early in data management workflows and eliminates the additional effort normally associated with publicly depositing research data.

We propose a neural network model for the jumping escape response behavior observed in the cricket cercal sensory system. This sensory system processes low-intensity air currents in the animal's immediate environment generated by predators, competitors, and mates. Our model is inspired by decades of physiological and anatomical studies. We compare the performance of our model with a model derived through a universal approximation, or a generic deep learning, approach, and demonstrate that, to achieve the same performance, these models required between one and two orders of magnitude more parameters. Furthermore, since the architecture of the bio-inspired model is defined by a set of logical relations between neurons, we find that the model is open to interpretation and can be understood. This work demonstrates the potential of incorporating bio-inspired architectural motifs, which have evolved in animal nervous systems, into memory efficient neural network models.