Bingni W. Brunton

The paper identifies a minimal central pattern generator circuit for fly walking using connectome simulations and experimental validation.

This study investigates whether pilifers provide mechanosensory feedback during floral tracking in hawkmoths, finding that they respond to lateral motions with rapid latency and extensive neural connections.

Seven high-speed cameras, a hybrid 2D/3D network and body-model inverse kinematics track 50 keypoints on freely moving flies; height and speed peak together at all running speeds.

TiDHy demixes simultaneous latent dynamical systems from mixed observations, capturing multiple timescales of variation in synthetic and real-world data.

The paper demonstrates that a virtual animal combining a worm connectome and fly biomechanics can produce realistic behavior without biological accuracy, highlighting risks of overinterpreting such models.

The paper demonstrates that a single arousal measurement can reconstruct spatiotemporal brain dynamics across multiple modalities in awake mice.

The paper identifies a neural circuit in Drosophila that suppresses movement-encoding proprioceptive signals during self-generated leg movements.

This study shows that neural subspaces for imagined movements in the parietal cortex remain stable over several years in humans, improving the long-term reliability of brain-machine interfaces.

This study shows the thalamus controls cortical dynamics across conscious states using fMRI, electrophysiology, and biophysical modeling.

This paper shows that feedback-driven neural dynamics limit fast motor learning in BCI tasks by constraining controllability and explaining learning variability.

This study uses a 3D kinematic model of fly walking to show that sensorimotor delays limit robust locomotion, with performance degrading when delays exceed physiological ranges.

The paper examines intraspecific variation in campaniform sensilla placement on hawkmoth wings, finding consistent regions but variable numbers and distributions, suggesting robustness in flight control feedback.

This study shows that nonuniform wing stiffness and damping improve mechanosensory feedback accuracy in insects and offer design insights for robotics.

The paper shows that timed activation of VIP neurons in the SCN and late afternoon light exposure can rescue dysregulated estrus cycles in mice.

The paper models neuronal receptive fields as random samples from distributions, showing improved performance in sensory encoding tasks.

The paper identifies glycinergic neurons in the avian lumbosacral organ (LSO) that express myosin7a and epsin, suggesting a mechanosensory role in avian spinal cord function.

This paper develops cross-modal, self-supervised neural decoders using multiple data streams to achieve high decoding accuracy without labeled data.

AJILE12 provides a large dataset of intracranial neural recordings and upper body pose data from 12 participants over 55 days, with metadata on movement and behavior.

Anipose provides a toolkit for markerless 3D pose estimation using 2D video, enabling analysis of animal movement and motor control.

The paper shows how wing structure and neural encoding interact to determine optimal sensing strategies for flight control in insects.

The paper analyzes neural variability during naturalistic arm movements using intracranial recordings and machine learning, finding significant variability across subjects and events.

This paper introduces HTNet, a neural decoder that generalizes across participants and recording modalities, achieving performance comparable to tailored decoders with minimal data.

SIESTA automates sleep staging in rodents using machine learning, achieving high F1 scores for wakefulness, REM, and NREM sleep stages.

This study investigates how hawkmoths use tactile feedback to locate nectaries in flowers, showing they actively explore and learn to find floral features efficiently.

This paper introduces a dynamic mode decomposition method to extract time-resolved resting state networks from fMRI data, showing improved reproducibility and individualized structure.