Tostado-Marcos, P. | bioRxiv Science

bioRxiv2026

Automated inference of respiratory and syringeal biomechanical trajectories from birdsong acoustics

Songbirds, in particular zebra finches (Taeniopygia guttata), provide a powerful model for investigating the neural mechanisms of learned vocal behavior. Researchers typically rely on the acoustic structure of birdsong to quantify vocal behavior. As a more direct measure of motor control, we present VIBE: Vocal acoustic Inversion to Biomechanical Estimates, an open-source pipeline that recovers the biomechanical control parameters of song production directly from the acoustic waveform. Biomechanical models of the songbird syrinx describe vocal production with two continuously varying parameters: and {beta}, representing subsyringeal air sac pressure and syringeal muscle tension, respectively. Recovering these parameters from song acoustics provides a motor-based coordinate system against which neural activity or other dependent variables can be directly compared. Because and {beta} are the coupled control parameters of a nonlinear oscillator, their joint recovery is non-trivial. VIBE addresses this through iterative optimization of the governing normal-form equations. We validate VIBE against recorded air sac pressure across 44 songs from twelve birds, showing that the recovered corresponds to empirically measured air sac pressure. Pairing VIBE with Neuropixels recordings from RA in five birds, we find that RA activity is well predicted by the recovered parameters, and that and {beta} add predictive power beyond the acoustic features of song. By recovering biomechanical control parameters from the acoustic signal, VIBE makes the biomechanical coordinate system of song production accessible to the broader songbird research community. New & NoteworthyVIBE provides a novel, fully automated pipeline to recover the biomechanical control parameters of the avian vocal organ, and {beta}, as continuously varying quantities from the raw acoustic waveform, making the full biomechanical model of song production accessible at the scale of modern datasets.

neuroscience↗

bioRxiv2024

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices. https://snel-repo.github.io/falcon/

neuroscience↗