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Distributed representation of limb motor programs in arrays of adjustable pattern generators.

N E Berthier, S P Singh, A G Barto

Journal of Cognitive Neuroscience

|August 27, 2013

Summary

This study presents an adjustable pattern generator (APG) array model for motor control. The model, inspired by neural anatomy, successfully learned to control a simulated limb, advancing understanding of neural mechanisms for motor programs.

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Area of Science:

Neuroscience
Computational Neuroscience
Robotics

Background:

Motor control research seeks to link abstract motor program concepts with underlying neural mechanisms.
Existing models often simplify the complex interplay between brain regions involved in motor execution.

Purpose of the Study:

To explore the relationship between motor program concepts and neural mechanisms using a computational model.
To extend the single adjustable pattern generator (APG) model to an array of APGs for controlling complex movements.

Main Methods:

Developed a sensorimotor network model inspired by the cerebellum, red nucleus, and motor cortex.
Proposed rubrocerebellar and corticocerebellar information processing modules functioning as APGs.
Simulated an APG array controlling a two degree-of-freedom limb in a computational framework.

Main Results:

The APG array model demonstrated the ability to learn and control simulated limb movement.
Distributed motor programs were adjusted through learning within the APG array.
The model showed plausible relationships to neural anatomy and physiology.

Conclusions:

The APG array model provides a viable computational framework for understanding neural generation of motor programs.
This work represents a step towards a more comprehensive model of sensorimotor control.
Further research can build upon this model for more complex motor tasks.