Motor primitives in space and time via targeted gain modulation in cortical networks. (December 2018) - Stroud, Jake; Porter, Mason; Hennequin, Guillaume; Vogels, Tim
Abstract Motor cortex (M1) exhibits a rich repertoire of neuronal 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 modulation of neuronal input–output gains in recurrent neuronal-network models with a fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, 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. Many behavioral tasks require fast, reliable switching of the shape and duration of cortical activity. Stroud et al. show that modulation of neural excitability in recurrent network models provides flexible spatiotemporal control of neural activity.