Adrián Palacios-Muñoz

How the brain enables individuals to adapt behavior to their partner is key to understanding social exchange. For example, courtship behavior involves sensorimotor processing of signals that can result in behavioral dialog between partners, such as stereotyped movements and singing. The courtship behavior of Drosophila melanogaster males with their partners, which are usually female but can also be male, involves singing. To investigate how behavioral feedback and sensorimotor processing contribute to flexible social interactions, we compared the courtship behavior and singing of male D. melanogaster towards males and females. Quantitative analysis of their interactions revealed that while underlying courtship and song rules are unaffected by the sex of the partner, the behavioral dynamics and song sequences differ by partner sex. This divergence stems from sex-specific behavioral feedback: females decelerate to song, while males orient towards the singer. Moreover, optogenetic manipulations reveal that the partners’ responses are driven by sex-specific neural circuits that link song detection with arousal and social decisions. Our findings demonstrate that flexible social behaviors can arise from fixed sensorimotor rules through a context-dependent selection facilitated by the partner’s behavioral feedback. More broadly, our results reveal compositionality as a key mechanism for achieving behavioral flexibility during complex social interactions such as courtship.

Animals need to integrate sensory information from multiple modalities to interact with their environment and with others. In male Drosophila , multiple sensory cues modulate social behaviors such as courtship and aggression, but the strategies by which multimodal cues are combined to inform social behavior are not known. Here, we focus on how courtship song and taste cues are integrated to shape the social behavior of male flies. By combining cue manipulations with computational modeling, we assessed the individual and combined contributions of song and taste. Our results reveal three context-specific integration strategies. Overall social interactions were driven by a linear combination of song and taste. Aggression was driven by song, independent of taste cues. Courtship was controlled by a nonlinear integration of song and female taste cues, with female taste flipping the valence of song from suppressing to enhancing courtship. Our results show how context-specific integration strategies allow animals to recognize social scenarios and produce flexible, context-appropriate behaviors.

Summary: Animals need to fine-control the speed and direction of locomotion to navigate complex and dynamic environments. To achieve this, they integrate multimodal sensory inputs with their internal drive to constantly adjust their motor output. This integration involves the interplay of neuronal populations across different hierarchical levels along the sensorimotor axis – from sensory, central, and modulatory neurons in the brain to descending neurons and motor networks in the nerve cord. Here, we characterize two populations of neurons that control distinct aspects of walking on different hierarchical levels in Drosophila . First, we use in-vivo electrophysiological recordings to demonstrate that moonwalker descending neurons (MDN) integrate antennal touch to drive changes in walking direction from forward to backward. Second, we establish DopaMeander as an important component in the control of forward walking through a combination of optogenetic activation, silencing, connectomics, and in-vivo recordings. These dopaminergic modulatory neurons drive forward walking with increased turning, and the activity of individual neurons is correlated with ipsiversive turning. Hence, MDN and DopaMeander control opposite regimes of walking on different hierarchical levels. Computational models reveal that their activity predicts key parameters of spontaneous walking. Moreover, we find that both MDN and DopaMeander are gated out during flight. This suggests that neuronal populations across levels of control are modulated by the behavioral state to minimize cross-talk between motor programs.

Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, and fast.We here introduce DeepAudioSegmenter (DAS), a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of DAS using acoustic signals with diverse characteristics from insects, birds, and mammals. DAS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations. The method can be trained to annotate signals from new species with little manual annotation and can be combined with unsupervised methods to discover novel signal types. DAS annotates song with high throughput and low latency for experimental interventions in realtime. Overall, DAS is a universal, versatile, and accessible tool for annotating acoustic communication signals.