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Brain theory and cooperative computation.

M A Arbib

    Human Neurobiology
    |January 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    This study contrasts top-down brain theory with bottom-up neural network analysis, proposing cooperative computation for integrating perceptual and motor schemas. This framework informs machine vision and visuomotor coordination research.

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    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Artificial Intelligence

    Background:

    • Distinguishes between
    • "top-down" brain theory (functional analysis of cognitive processes via schemas) and "bottom-up" brain theory (neural network dynamics).
    • Proposes "cooperative computation" as the interaction style for neural subsystems across different levels.

    Purpose of the Study:

    • To introduce perceptual and motor schemas as fundamental units for environmental representation and movement control.
    • To demonstrate the application of cooperative computation in machine vision systems.
    • To illustrate the interplay between top-down analysis and neural modeling using visuomotor coordination examples.

    Main Methods:

    • Functional analysis of cognitive processes using interacting schemas.

    Related Experiment Videos

  • Analysis of neural network dynamics.
  • Development of algorithms for optic flow computation and knowledge representation for visual scene interpretation.
  • Study of visuomotor coordination in amphibians.

    • Main Results:

    • Perceptual schemas are proposed as building blocks for environment representation.
    • Motor schemas are presented as control systems for movement coordination.
    • A cooperative computation model provides insights into hierarchical neural structure evolution and machine vision scene interpretation.
    • Visuomotor coordination in frogs and toads exemplifies the interaction between top-down analysis and neural modeling.

    Conclusions:

    • Cooperative computation offers a unified framework for understanding brain function and designing intelligent systems.
    • The schema-based approach, combined with neural modeling, advances our understanding of perception, action, and their coordination.
    • This research bridges cognitive theory and computational neuroscience, with implications for artificial intelligence and robotics.