Asahina, K. | Search bioRxiv

bioRxiv2020

A neurogenetic mechanism of experience-dependent suppression of aggression

Aggression is an ethologically important social behavior1 but excessive aggression can be detrimental to animal fitness2,3. Social experiences among conspecific individuals reduce aggression in a wide range of animals4. However, the genetic and neural basis for the experience-dependent suppression of aggression remains largely unknown. Here we found that nervy (nvy), a Drosophila homolog of vertebrate myeloid translocation gene (MTG)5 involved in transcriptional regulation6-8, suppresses aggression via its action in a specific subset of neurons. Loss-of-function mutation of the nvy gene resulted in hyper-aggressiveness only in socially experienced flies, whereas overexpression of nvy suppressed spontaneous aggression in socially naive flies. The loss-of-function nvy mutant exhibited persistent aggression under various contexts in which wild-type flies transition to escape or courtship behaviors. Knockdown of nvy in octopaminergic/tyraminergic (OA/TA) neurons increased aggression, phenocopying the nvy mutation. We found that a subpopulation of OA/TA cells specifically labeled by nvy is required for the social-experience-dependent suppression of aggression. Moreover, cell-type-specific transcriptomics on nvy-expressing OA/TA neurons revealed aggression-controlling genes that are likely downstream of nvy. Our results are the first to describe the presence of a specific neuronal subpopulation in the central brain that actively suppresses aggression in a social-experience-dependent manner, illuminating the underlying genetic mechanism.

neuroscience↗

bioRxiv2020

Quantitative comparison of Drosophila behavior annotations by human observers and a machine learning algorithm

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.Significance Statement Accurate quantification of animal behaviors is fundamental to neuroscience. Here, we quantitatively assess how human choices influence the performance of automated classifiers trained by a machine-learning algorithm. We found that human decisions about the computational tracking method, the training images, and the images used for performance evaluation impact both the classifier outputs and how human observers interpret the results. These factors are sometimes overlooked but are critical, especially because animal behavior is itself inherently variable. Automated quantification of animal behavior is becoming increasingly prevalent: our results provide a model for bridging the gap between traditional human annotations and computer-based annotations. Systematic assessment of human choices is important for developing behavior classifiers that perform robustly in a variety of experimental conditions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗