Andrew B. Schwartz

This study shows humans predictively adjust arm stiffness before movement to optimize task success, even at the cost of energy efficiency.

The paper develops hybrid neural network models to simulate cortical neuronal activity during movement, showing that synaptic processes drive state transitions in neural networks.

The paper trains an autoregressive Transformer on neural spiking data to decode motor behavior, showing broad utility across tasks but highlighting limitations in scaling.

The paper shows that stiffness, a mechanical property, is used to coordinate force and displacement during object manipulation by modulating muscle activity.