A neural circuit for context-dependent multimodal signaling in Drosophila

Elsa Steinfath, Afshin Khalili, Melanie Stenger, Bjarne L. Schultze, Sarath Ravindran Nair, Kimia Alizadeh and Jan Clemens ()
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Elsa Steinfath: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Afshin Khalili: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Melanie Stenger: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Bjarne L. Schultze: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Sarath Ravindran Nair: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Kimia Alizadeh: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences
Jan Clemens: a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Many animals produce multimodal displays that combine acoustic, visual, or vibratory signals, yet the neural mechanisms coordinating these behaviors remain unclear. Using Drosophila courtship as a model, we reveal how a single neural circuit integrates sensory cues and motivational state to orchestrate multimodal signaling. Male flies produce both air-borne song and substrate-borne vibrations during courtship, but in distinct, largely non-overlapping contexts. We demonstrate that the same brain neurons that drive song also control vibrations through separate pre-motor pathways, with cell-type specific dynamics. This shared circuit coordinates multimodal displays with locomotion, ensuring vibrations are produced only when they can effectively reach the female. The circuit employs shared motifs—recurrence and mutual inhibition—that enable dynamic control of multimodal signals by external cues and internal state. A computational model confirms that these motifs are sufficient to explain the observed behavioral dynamics. Our findings illustrate how simple neural circuit elements can be combined to select and coordinate complex multimodal behaviors.

Date: 2025
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DOI: 10.1038/s41467-025-64907-9

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