Topic: low-rank network architecture

From catching a baseball intercepting an opponent at karate, movement control in a dynamic setting often requires motor planning in prediction of future target motion. To understand how the brain to form anticipatory motor plan, we trained a modular recurrent neural network model to output similar hand trajectories as macaque monkeys in a manual interception task. In the network modules designed to model the dorsal premotor (PMd) and primary motor (M1) cortices, we observed that rotational neural dynamics during motor preparation transitioned into a subspace encoding hand positions during exec