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[Work of large information systems]

S V Fomin, V L Dunin-Barkovskiĭ, A N Chetaev

    Biofizika
    |May 1, 1976
    PubMed
    Summary
    This summary is machine-generated.

    This study applies information theory to large systems like the brain, proposing a coding theorem for informational collectives. Stochastic neural connections are shown to be reasonable from an information theory perspective.

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    Area of Science:

    • Neuroscience
    • Information Theory
    • Computational Neuroscience

    Context:

    • The brain as a large informational system.
    • Challenges in understanding neural computation with noise.
    • Existing theories of reliable computation.

    Purpose:

    • To apply informational ideas to study large systems like the brain.
    • To develop a coding theorem for "informational collectives."
    • To analyze the role of stochastic neural connections.

    Summary:

    • Proposes a "material analogy" for intuitive understanding of Shannon's information theory.
    • Formulates a coding theorem for "informational collectives" (large groups of information sources and receivers).
    • Demonstrates that stochastic neural connections in neuronal structures are justifiable from an information theory standpoint.

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    Impact:

    • Provides a theoretical framework for understanding information processing in complex neural systems.
    • Offers insights into the functional significance of randomness in neural connections.
    • Connects principles of reliable computation with neural network architecture.