J. Andrew Pruszynski

This paper introduces DynISys, a model that infers neural dynamics and communication from multi-area recordings, revealing mechanisms for motor planning and execution.

The paper investigates how the brain encodes bimanual movement directions using fMRI, finding distinct neural representations for unimanual and bimanual movements.

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 investigates whether increased reaction time for longer movement sequences reflects sequence preparation or first-target ambiguity, finding that RT increases are fully explained by ambiguity, not sequence preparation.

The paper investigates how sensory cues and memory influence sequential movement production, showing that integration of both sources changes with learning and predictability.

The paper tests how movement speed affects edge-orientation discrimination during active tactile exploration, finding improved performance at very slow speeds and worse performance at faster speeds.

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

The paper develops a transient marmoset model of myofascial temporomandibular disorder using nerve growth factor injections, showing mechanical hypersensitivity and circadian changes without jaw dysfunction.

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

This paper shows that movement duration in reaching tasks can be explained by infinite horizon optimal feedback control, capturing tradeoffs between duration, accuracy, and task parameters.

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

The paper investigates how repetition of movement sequences affects performance, finding that memory traces are activated when the last 2-3 movements match the current execution.

This study disentangles the mechanisms of motor sequence learning by distinguishing between anticipation of next actions and optimization of movement trajectories using a continuous reaching task.

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 investigates movement strategies for detecting microscale tactile features and finds that participants can detect features as small as 2 micrometers using a single contact event with optimized movement control.

This study examines how adult common marmosets perform a string-pulling task and how their performance changes across the lifespan.

The paper investigates how intrinsic spike frequency adaptation (I-SFA) and inhibitory circuit dynamics contribute to extrinsic-SFA (E-SFA) in primate lateral prefrontal cortex neurons during behavior.

The paper investigates how the brain plans multiple future arm movements during sequential actions, finding that planning processes interact and influence each other.

The paper demonstrates that increased spinal reflex responses to loads reduce motor cortex activity, using a neural network model to show how lower-level feedback influences goal-directed motor corrections.

The paper investigates neural mechanisms of online action preparation during sequential movements, finding increased activation in parietal and visual areas for overlapping actions, while premotor cortex remains unaffected.

This study shows that Rhesus Macaques exhibit express visuomotor responses in upper limb muscles with latencies of ~65 ms, similar to humans but faster, suggesting shared but distinct visuomotor transformation mechanisms.

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

This study shows that reach corrections toward moving objects are faster than toward jumping targets, suggesting that continuous motion cues improve motor adaptation.

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

This study investigates how reward affects seven sensorimotor feedback loops in humans, finding that faster loops are insensitive to reward while slower loops show latency reductions.

This paper shows human first-order tactile neurons signal edge orientation differences and maintain this signaling across various scanning speeds through sequential spike train structures.

This study uses fMRI to show non-linear interactions between fingers in human S1 and M1, revealing how sensory inputs are integrated for dexterous manipulation.

The paper uses fMRI to show that motor planning activates specific patterns in primary motor and somatosensory cortex, similar to those during movement execution.

The paper examines the spatial resolution of FA-1 and SA-1 tactile neurons using raised dots, finding their resolution corresponds to a spatial period of ~0.4 mm.