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Coordinate-free sensorimotor processing: computing with population codes.

Pietro G. Morasso1, Vittorio Sanguineti, Francesco Frisone

  • 1Department of Informatics, Systems and Telecommunications, University of Genova, Genova, Italy

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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This study introduces a novel computational architecture for processing sensorimotor patterns. It utilizes population codes and cortical map dynamics for intelligent planning and goal-oriented functions.

Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Robotics

Background:

  • Intelligent systems require sophisticated processing of sensorimotor patterns.
  • Internal representations of the external world are crucial for planning and goal-oriented behaviors.
  • Existing models often lack a unified framework for handling complex sensorimotor data.

Purpose of the Study:

  • To propose a computational architecture for intelligent sensorimotor pattern processing.
  • To investigate the nature of internal representations for planning and goal-oriented functions.
  • To develop a biologically plausible model integrating cortical map dynamics and self-organization.

Main Methods:

  • A novel global architecture for coordinate-free processing of sensorimotor patterns.

Related Experiment Videos

  • Utilizing population codes as distributed internal representations of external variables.
  • Employing coupled dynamics of cortical maps for trajectory formation.

    • Main Results:

    • Demonstration of basic computational features in articulatory speech synthesis.
    • Evaluation of metric properties through simple simulation studies.
    • A biologically plausible model integrating diverse concepts in computational neuroscience.

    Conclusions:

    • The proposed architecture offers a coordinate-free approach to sensorimotor pattern processing.
    • Population codes and coupled cortical map dynamics provide a robust framework for internal representations and trajectory formation.
    • The model shows promise for applications in intelligent systems and understanding brain function.