Motor neural null space

Background:

The investigation of neural populations dynamics has shed light on the existence of low-dimensional spaces capable of explaining most of the neural activity. These spaces are now commonly called neural manifolds. Neural manifolds are in general identified through optimization techniques based on dimensionality reductions that can be both linear and non-linear. These insights allow us to understand how similar patterns of activity (neural modes) are used to generate different output in terms of behavioral response. Some of the strongest results obtained through this approach include the finding that preparatory movement activity lies on an orthogonal plane with respect to executory activity, the fact that a single consistent neural manifold can explain different hand/fingers movements or yet the overlapping between a neural manifold spanning executory activity with the manifolds explaining observed or imagined movements.The identification of such low-dimensional manifolds not only help elucidate neural correlates of behavior, but it also holds great translational potential in what it can help the development of innovative brain-machine interfaces (BMIs) both in the frames of motor restoration and motor augmentation. However, most of these findings have relied on multi-unit neural activities, often recorded in non-human primates. To increase the translational.