Finding food is a vital skill and a constant task for any animal, and associative learning of food-predicting cues gives an advantage in this daily struggle. The strength of the associations between cues and food depends on a number of parameters, such as the salience of the cue, the strength of the food reward and the number of joint cue-food experiences. We investigate what impact the strength of an associative odour-sugar memory has on the microbehaviour of Drosophila melanogaster larvae. We find that larvae form stronger memories with increasing concentrations of sugar or odour, and that these stronger memories manifest themselves in stronger modulations of two aspects of larval microbehaviour, the rate and the direction of lateral reorientation manoeuvres (so-called head casts). These two modulations of larval behaviour are found to be correlated to each other in every experiment performed, which is in line with a model that assumes that both modulations are controlled by a common motor output. Given that the Drosophila larva is a genetically tractable model organism that is well suited to the study of simple circuits at the single-cell level, these analyses can guide future research into the neuronal circuits underlying the translation of associative memories of different strength into behaviour, and may help to understand how these processes are organised in more complex systems.
Avoiding associatively learned predictors of danger is crucial for survival. Aversive memories can, however, become counter-adaptive when they are overly generalized to harmless cues and contexts. In a fruit fly odor–electric shock associative memory paradigm, we found that learned avoidance lost its specificity for the trained odor and became general to novel odors within a day of training. We discuss the possible neural circuit mechanisms of this effect and highlight the parallelism to over-generalization of learned fear behavior after an incubation period in rodents and humans, with due relevance for post-traumatic stress disorder.
Background: Dysfunction in the endolysosome, a late endosomal to lysosomal degradative intracellular compartment, is an early hallmark of some neurodegenerative diseases, in particular Alzheimer's disease. However, the subtle morphological changes in compartments of affected neurons are difficult to quantify quickly and reliably, making this phenotype inaccessible as either an early diagnostic marker, or as a read-out for drug screening.
This article aims to present an experimental evaluation of an offline, geometry-aware aerial visual inspection framework, specifically in constrained environment, established for geometrically fractured objects, by employing an autonomous unmanned aerial vehicle (UAV), equipped with on-board sensors. Based on a model-centric approach, the proposed inspection framework, generates inspection viewpoints around the geometrically fractured object, subject to the augmented static bounds to prevent collisions. The novel framework of visual inspection, presented in this article, aims to mitigate challenges arising due to the spatially-constrained environment, such as limited configuration space and collision with the object under inspection, by accounting for the geometrical information of the vehicle to be inspected. The efficacy of the proposed scheme is experimentally evaluated through large scale field trials with a mining machine.
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