Sensory-driven modulation of song structure and amplitude in Drosophila melanogaster

Abstract

A fundamental goal of neuroscience is to understand how sensory inputs are transformed into behavioral outputs. Mounting evidence suggests that brains are optimized to process naturalistic stimuli, but natural behaviors involving neural computations of interest are often challenging to accurately quantify. Acoustic communication is a natural behavior which exists throughout the animal kingdom, and can involve a dynamic signal which is both highly quantifiable and necessitates complex neural networks for generation and perception. Here, we use quantitative behavioral assays, computational modeling, and genetic manipulations to dissect the neural computations and circuits underlying this behavior in Drosophila melanogaster.

Drosophila acoustic communication consists of a patterned courtship song produced by males to increase their chance of success with a female. Songs comprise of two modes-pulse and sine-and males alternate between these modes to produce patterns with high variability. Neural noise within pattern generating circuits is widely assumed to be the primary source of such variability. In contrast, we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male.

Several organisms modulate the amplitude of their acoustic signal in accordance with target distance to optimize for receiver intensity and conserve energy. Previously, this sensorimotor transformation has not been identified in invertebrates. We not only demonstrate that male flies increase pulse amplitude with female distance, but that this sensorimotor transformation takes place within ~35ms. Further, through genetic and physical manipulations, we establish that amplitude modulation at large distances is exclusively mediated by vision, dependent on naturalistic stimuli, and implemented via the indirect flight muscles.

These results demonstrate that detailed quantification and modeling can reveal unexpected neural complexity underlying seemingly "stereotyped" behaviors.