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Modeling sensorimotor learning with linear dynamical systems.

Sen Cheng1, Philip N Sabes

  • 1Sloan-Swartz Center for Theoretical Neurobiology, W. M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, University of California, San Francisco, 94143-0444, USA. chengs@phy.ucsf.edu

Neural Computation
|February 24, 2006
PubMed
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Linear dynamical systems (LDS) offer a powerful framework for understanding sensorimotor learning dynamics. This study presents an expectation-maximization algorithm for fitting LDS models, providing deeper insights into adaptation processes than traditional methods.

Area of Science:

  • Neuroscience
  • Motor Control
  • Machine Learning

Background:

  • Sensorimotor learning involves adapting motor output based on sensory feedback.
  • Previous models often focus on steady-state adaptation, overlooking dynamic processes.
  • Linear dynamical systems (LDS) offer a promising approach to model trial-by-trial learning dynamics.

Purpose of the Study:

  • To explore the theoretical and practical aspects of using linear dynamical systems (LDS) for sensorimotor learning.
  • To develop and present an improved method for fitting LDS models to experimental data.
  • To demonstrate the utility of LDS in analyzing adaptation dynamics and quantifying learning parameters.

Main Methods:

  • Utilized linear dynamical systems (LDS) to model the trial-by-trial changes in sensorimotor transformations.

Related Experiment Videos

  • Introduced an expectation-maximization (EM) algorithm for fitting LDS models to experimental data, addressing limitations of linear regression.
  • Applied the developed methods to analyze adaptation to shifted visual feedback during reaching tasks.

    • Main Results:

    • Modeling trial-by-trial dynamics with LDS provides a richer understanding of adaptation compared to steady-state analysis.
    • LDS models can quantify sensory and performance biases, decay of learned transformations, and sources of motor variability.
    • The proposed EM algorithm yields more consistent parameter estimates for LDS models than previous regression-based approaches.

    Conclusions:

    • Linear dynamical systems provide a robust framework for dissecting the complexities of sensorimotor adaptation.
    • The expectation-maximization algorithm is a suitable method for estimating parameters in LDS models of learning.
    • This approach enhances the analysis of motor learning, offering quantitative insights into dynamic adaptation processes.