Jonathan A. Michaels

The paper investigates how probabilistic predictions influence feedback responses in the human brain using fMRI, showing expected sensory signals and prediction error signals in motor and somatosensory cortices.

This study shows that reward enhances auditory pattern encoding in the primate globus pallidus, with responses persisting even after reward is removed.

The paper shows that recurrent neural networks (RNNs) exhibit motor learning savings without explicit contextual cues, suggesting a neural basis for memory retention through high-dimensional activity shifts.

EMUsort improves MUAP spike sorting accuracy by addressing challenges like long delays, complex waveforms, and high overlap in motor unit recordings.

ATHENA provides an accurate, open-source tool for 3D markerless hand tracking, validated against optoelectronic systems with high spatiotemporal agreement and reduced costs.

This study shows that sensory expectations influence motor preparation and corrective responses in humans and monkeys, with neural activity scaling with perturbation probabilities.

This study investigates neural population dynamics in motor cortex during reaching, revealing a compositional structure that enables posture-dependent control.

This paper introduces a new paradigm to study muscle activity pattern learning, finding that difficulty is linked to muscle activity similarity to everyday use and activity size.

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

The paper introduces Myomatrix arrays, a high-resolution electrode technology for recording muscle activity from motor units across species during natural behaviors.

MotorNet is a Python toolbox that enables training artificial neural networks on differentiable biomechanical effectors for motor control tasks.

This study develops a modular neural network model to simulate grasp movement generation in macaques by mimicking the anatomical circuit and using visual features to generate muscle dynamics.

The paper investigates how preparation time affects grasping movements in macaques by analyzing neural activity in premotor and parietal cortex, revealing distinct dynamics in movement anticipation and execution.

This study investigates how grasp and laterality-related information is encoded in the macaque fronto-parietal network during a delayed grasping task.