PhD Birdsong Biomechanics and Neuroscience

Neural control and biomechanics of birdsong: linking song-control circuits to vocal apparatus biomechanics

Context and state of the art
Vocal communication is essential for survival and plays a central role in Evolution. Birdsong provides a powerful model for understanding how fine motor skills emerge from interactions between the brain, body, and behavior. Learned vocal signals arise from the coordinated activity of specialised forebrain circuits, sensorimotor integration, and the dynamics of the vocal apparatus. In songbirds, these processes shape vocal behaviour and produce precisely structured acoustic signals used for social communication.Despite major advances in both the neurobiology of birdsong and the biomechanics of vocal production, these two fields have largely developed independently. As a result, we still lack a clear understanding of how neural activity is translated into coordinated movements of the vocal organs, and how these interactions generate sound. Recent methodological advances now make it possible to address this question by combining approaches across levels of organisation.

Scientific objectives and hypotheses
This project aims to to establish a direct, causal link between neural activity in the song-control system and the biomechanics of vocal production. Specifically, it seeks to uncover how distinct song-control brain regions (nuclei) influence the movements of the syrinx and upper vocal tract (trachea, larynx, tongue, beak), and how these movements shape the acoustic structure of song.We hypothesize that distinct nuclei contribute in specific and measurable ways to vocal motor control, leading to predictable changes in both kinematics and sound output. More specifically, we will test the following hypotheses:
1.       Functional specialization of song nucleiDistinct song-control nuclei exert specific and dissociable influences on vocal motor output. Perturbing a specific nucleus will induce characteristic and reproducible changes in the kinematics of the syrinx and upper vocal tract.
2.       Predictable neuromechanical mappingChanges in neural activity lead to structured and quantifiable transformations of vocal organ kinematics, which in turn produce predictable modifications in acoustic features. This implies the existence of a consistent mapping between neural perturbations, biomechanics, and sound.
3.       Temporal precision of motor controlBecause birdsong relies on fine temporal patterning, brief, syllable-locked perturbations will induce rapid and time-specific changes in vocal kinematics, revealing the dynamics of neural control at the millisecond scale.

cientific approach and methodology
To test these hypotheses, we will combine targeted neurophysiological manipulations (lesions and transient, syllable-locked electrical stimulations) with high-speed 3D X-ray imaging in singing birds, with finite element modelling of the vocal apparatus and behavioural analyses. We will use zebra finches, a reference bird model for both birdsong neurophysiology and biomechanics This interdisciplinary approach will allow us to reconstruct the full spatiotemporal cascade from neural perturbation to movement and sound.

Risks and alternative strategies
A first risk is that neural perturbations (lesions or stimulations) may produce weak or variable effects on vocal output. To mitigate this, we will use complementary approaches (both lesions and temporally precise stimulations) and target multiple song-control nuclei, increasing the likelihood of observing robust and interpretable effects. If behavioural effects remain subtle, analyses will focus on fine-scale kinematic and acoustic variations, which are often more sensitive to perturbation than global song structure.A second risk relates to the technical challenges of high-resolution 3D X-ray tracking in animals that are moving freely. This challenging method is currently being optimised in PP's laboratory, with successful results achieved in terms of accurate synchronised kinematics and acoustic data obtained from singing canaries and zebra finches. This demonstrates the feasibility of this approach.More generally, the project is designed with methodological redundancy, ensuring that meaningful results can be obtained even if one component proves technically limiting.

Expected results and deliverables
The project will generate a unique, multimodal dataset combining neural perturbations, 3D kinematics of the vocal apparatus, and acoustic recordings in freely singing birds. Expected outcomes include:
·        Quantitative characterisation of how specific brain nuclei influence vocal organ kinematics
·        Identification of causal relationships between neural activity, biomechanics, and acoustic structure
·        Development of integrative models linking neural control to vocal output
·        High-impact publications in journals spanning neurobiology and biomechanics journalsIn addition, the project will produce curated datasets and analysis pipelines that can be shared with the scientific community, contributing to reproducibility and future comparative studies.

Broader impact
By uncovering how neural commands are transformed into coordinated movements and sound, this project will advance our understanding of the general principles underlying motor control. While focused on birdsong, these principles are expected to extend to other complex behaviours, including human speech.The project also contributes to bridging traditionally separate disciplines, fostering integration between neurobiology, biomechanics, and computational modelling. This interdisciplinary framework may inspire new approaches in bio-inspired robotics, motor control theory, and the study of sensorimotor disorders.Finally, the development of combined neurophysiological and 3D imaging approaches opens new methodological perspectives for studying behaviour in a wide range of systems.

Provisional timeline
Year 1: Setup of experimental protocols, training in both laboratories, optimisation of neural perturbation techniques and X-ray imaging. Initial data acquisition and pilot analyses.
Year 2: Systematic data collection combining neural manipulations, 3D kinematics, and acoustics. Development of analysis pipelines and first integrative results.
Year 3: Data integration and modelling of neuromechanical relationships. Manuscript preparation, dissemination of results, and finalisation of the thesis.

Interdisciplinary framework and supervision
This project integrates two complementary fields, neurophysiology and functional biomechanics, and is co-supervised by two researchers whose expertise enables genuinely interdisciplinary training:
·     Pauline Provini (PP), Muséum national d’Histoire naturelle – MECADEV – Specialist in 3D biomechanics of the avian vocal apparatus. PP and her team will supervise biplanar X-ray imaging, 3D kinematic reconstruction, and computational modelling of vocal tract dynamics. Relevant publications: Provini et al., 2022, 2023; Kazemi et al., 2023; Fournier et al., 2024. Personal webpage
·     Nicolas Giret (NG), CNRS – NeuroPSI – Expert in birdsong neurophysiology, including targeted brain manipulations, electrophysiology, and behavioural analysis. NG and his team will supervise intracerebral manipulations (lesions, targeted stimulations) and associated neurophysiological analyses. Relevant publications: Giret et al., 2014; Zai et al., 2024; Lorenz et al., 2025; Rolland et al., 2025. Team webpage 

Doctoral training
The student will receive interdisciplinary training combining neurophysiology, biomechanics and modelling. The student will gain hands-on expertise in experimental neurophysiology, including targeted brain manipulations and stimulation protocols under the supervision of NG, and in 3D X-ray imaging, quantitative biomechanical analyses, and computational modelling under the supervision of PP.This training will equip the student with a strong, hybrid skill set bridging neuroethology, biomechanics, and computational analysis.Student profileThe candidate must hold or be in the process of obtaining a Master’s degree in Neuroscience, Biomechanics, Computational biology and/or Engineering. We are seeking a motivated candidate willing to work across two laboratories and engage with a range of experimental approaches.  The ideal candidate should have a solid background in either neural or biomechanical sciences and demonstrate clear motivation and aptitude for interdisciplinary training. The successful candidate should also demonstrate strong organizational skills, the ability to work independently, proficiency in statistical analysis and English, as well as excellent writing skills. Prior experience with birds will be considered additional assets. Speaking French is not mandatory.If you wish to apply, please send your CV, a cover letter, and your academic transcripts for both your Bachelor’s and Master’s degrees by April 12, 2026, to Pauline Provini (pauline.provini@mnhn.fr) and Nicolas Giret (nicolas.giret@cnrs.fr).  If your application is selected, we will assist you in preparing your application to the doctoral program (more info by clicking here ). The entrance examination will take place on May 12, 2026. 

References
1.       Provini, P., Brunet, A., Filippo, A. & Van Wassenbergh, S. In vivo intraoral waterflow quantification reveals hidden mechanisms of suction feeding in fish. Elife 11, e73621 (2022).
2.       Provini, P., Camp, A. L. & Crandell, K. E. Emerging biological insights enabled by high-resolution 3D motion data: promises, perspectives and pitfalls. J. Exp. Biol. 226, jeb245138 (2023).
3.       Kazemi, A., Kesba, M. & Provini, P. Realistic three-dimensional avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus). J. R. Soc. Interface 20, 20220728 (2023).
4.       Fournier, M., Olson, R., Van Wassenbergh, S. & Provini, P. The avian vocal system: 3D reconstruction reveals upper vocal tract elongation during head motion. J Exp Biol 227, jeb247945 (2024).
5.       Giret, N., Kornfeld, J., Ganguli, S. & Hahnloser, R. H. R. Evidence for a causal inverse model in an avian cortico-basal ganglia circuit. Proc. Natl. Acad. Sci. 111, 6063–6068 (2014).
6.       Zai, A. T., Stepien, A. E., Giret, N. & Hahnloser, R. H. Goal-directed vocal planning in a songbird. Elife 12, RP90445 (2024).
7.       Lorenz, C. et al. Sharp waves, bursts, and coherence: Activity in a songbird vocal circuit is influenced by behavioral state. J. Neurosci. 45, (2025).
8.       Rolland, M., Zai, A. T., Hahnloser, R. H., Del Negro, C. & Giret, N. Visually-guided compensation of deafening-induced song deterioration. Front. Psychol. 16, 1521407 (2025).