Self-configuring feedback loops for sensorimotor control

Abstract
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Abstract

How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 minutes of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum).

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript.The source code to generate all figures is available as two commented Jupyter notebooks. They can be downloaded from the following repository:https://gitlab.com/sergio.verduzco/public_materials/-/tree/master/adaptive_plasticityInstructions are in the "readme.md" file. Briefly:Prerequisites for running the notebooks are:- Python 3.5 or above (https://www.python.org)- Jupyter (https://jupyter.org)- Draculab (https://gitlab.com/sergio.verduzco/draculab)Please see the links above for detailed installation instructions.

Article and author information

Author details

Sergio Oscar Verduzco-Flores

Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Japan

For correspondence
sergio.verduzco@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0712-145X
Erik De Schutter

Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Japan

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-8618-5138

Funding

No external funding was received for this work.

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This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.