Motor primitives in space and time via targeted gain modulation in cortical networks
By Jake P. Stroud, M. A. Porter, Guillaume Hennequin, Tim P. Vogels. 2018
119 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience
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In Cognitive Neuroscience
- Friedemann Zenke
- Tim P. Vogels
- Johannes von Oswald
- Christian Henning
- João Sacramento
- Benjamin F. Grewe
- Wulfram Gerstner
- Xiao-Jing Wang
Citations in the corpus
- Continual learning with hypernetworks (2019, positive): “This interpretation is consistent with a growing body of work which suggests the involvement of modulatory inputs in implementing context- or task-dependent network mode-switching [29, 30, 31].”
- The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks (2020, background): “…2013), medial frontal (Wang, Narain, Hosseini, & Jazayeri, 2018), and motor areas (Michaels, Schaffelhofer, Agudelo-Toro, & Scherberger, 2019; Stroud, Porter, Hennequin, & Vogels, 2018), thus providing us with new vistas for understanding the dynamic properties of computation in recurrent…”