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InFoRM: a unified inverse and forward model for sensorimotor control.

Myriam Lauren de Graaf1,2,3, Lena Kloock4, André Schwarze4

  • 1Department of Movement Science, University of Münster, Horstmarer Landweg 62b, 48149, Münster, Germany. mdegraaf@uni-muenster.de.

Scientific Reports
|March 10, 2026
PubMed
Summary
This summary is machine-generated.

A novel unified sensorimotor model, the inverse-forward-recognition model (InFoRM), integrates inverse and forward functions. This integrated approach outperforms separate models in reproducing movements and generalizes to new directions.

Keywords:
Forward modelInternal modelsInverse modelMotor controlNeural networksReservoir computing

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Area of Science:

  • Computational Neuroscience
  • Robotics
  • Motor Control

Background:

  • Traditional sensorimotor control models utilize separate inverse and forward internal models.
  • The neural basis for separating these models remains unclear, and separate networks may increase computational cost.
  • Investigating unified models could offer computational advantages and insights into neural processing.

Purpose of the Study:

  • To investigate if inverse and forward sensorimotor functions can be integrated within a single neural circuit.
  • To introduce and evaluate an inverse-forward-recognition model (InFoRM) for sensorimotor control.
  • To compare the performance and resource efficiency of InFoRM against classical separated models.

Main Methods:

  • Implemented InFoRM using neural networks.
  • Compared InFoRM with control architectures based on separated inverse and forward models.
  • Utilized recorded 3D kinematics for desired trajectories and inverse dynamics to derive efferent and afferent signals.

Main Results:

  • InFoRM significantly outperformed control architectures in reproducing cyclic reaching movements across various conditions.
  • The InFoRM required fewer computational resources compared to separated models.
  • InFoRM demonstrated generalization capabilities, morphing to untrained movement directions and generating novel motor commands and predicted feedback.

Conclusions:

  • Integrating inverse and forward sensorimotor processes within a single neural network (InFoRM) offers significant computational advantages.
  • Unified sensorimotor models may be more efficient and flexible than traditional separated models.
  • These findings suggest that exploring unified neural circuits for sensorimotor control is a promising research direction.