Daniel B. Rubin

Learning-related population dynamics in right and left dorsal premotor cortex during typing skill acquisition

The paper shows that during typing skill acquisition using a BCI, neural population dynamics in the dorsal premotor cortex become more compact, with left hemisphere showing enhanced firing-rate modulation and generalization.

Observation-Related Activity in Human Motor Cortex Increases with Effector Anthropomorphicity

The paper investigates how motor cortex activity changes during observation of different anthropomorphic effectors, finding increased neural modulation with greater human-likeness of the observed agents.

Cross-brain transfer of high-performance intracortical speech and handwriting BCIs

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

Decoding intended speech with an intracortical brain-computer interface in a person with longstanding anarthria and locked-in syndrome

This paper decodes intended speech from a person with anarthria and locked-in syndrome using an intracortical brain-computer interface, showing that phonemes, words, and higher-order language units can be decoded above chance.

Stabilizing brain-computer interfaces through alignment of latent dynamics

This paper introduces NoMAD, a method that stabilizes brain-computer interfaces by aligning latent dynamics, achieving stable decoding without supervised recalibration over long periods.

A mosaic of whole-body representations in human motor cortex

The paper maps human motor cortex using microelectrode recordings, finding whole-body representations and challenging the traditional motor homunculus model.

Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans

This study shows that co-occurring ripple oscillations enhance neuronal interactions between cortical locations in humans by facilitating prediction and co-firing of neurons.

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

This paper develops a continuous resting-state EEG model to predict recovery from disorders of consciousness after acute brain injury, achieving high accuracy across multiple time horizons.

A unified circuit model of attention: Neural and behavioral effects

This paper shows a circuit model of attention explains both neural and behavioral effects of visual attention across different experimental paradigms.

Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex

The paper shows that a stochastic Stabilized Supralinear Network model explains stimulus-induced changes in cortical variability and correlations.