Gallego, Juan A., Matthew G. Perich, Lee E. Miller, and Sara A. Solla. “Neural manifolds for the control of movement.” Neuron 94, no. 5 (2017): 978-984.

Abstract

The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the “neural modes.” We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.

For further info, please read the paper Gallego et al. 2017.

Gallego, Juan A., Matthew G. Perich, Lee E. Miller, and Sara A. Solla. “Neural manifolds for the control of movement.” Neuron 94, no. 5 (2017): 978-984.

Related work about motor control using Recurrent Neural Networks see Huh and Todorov 2009.

Huh, D. and Todorov, E., 2009, March. Real-time motor control using recurrent neural networks. In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning(pp. 42-49). IEEE.