RT Journal Article
SR Electronic
T1 Motor primitives in space and time via targeted gain modulation in cortical networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 451054
DO 10.1101/451054
A1 Jake P. Stroud
A1 Mason A. Porter
A1 Guillaume Hennequin
A1 Tim P. Vogels
YR 2018
UL http://biorxiv.org/content/early/2018/10/24/451054.abstract
AB Motor cortex (M1) exhibits a rich repertoire of activities to support the generation of complex movements. Although recent neuronal-network models capture many qualitative aspects of M1 dynamics, they can generate only a few distinct movements. Additionally, it is unclear how M1 efficiently controls movements over a wide range of shapes and speeds. We demonstrate that simple modulation of neuronal input–output gains in recurrent neuronal-network models with fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, we show that a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, it is possible to assemble novel movements from previously learned primitives, and one can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.