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Neural modeling and model identification.

A C Sanderson, R J Peterka

    Critical Reviews in Biomedical Engineering
    |January 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    Neural modeling offers a quantitative approach to understanding the nervous system. This review covers models for neural firing, interactions, and coding, linking them to experimental data for a clearer picture of brain function.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Systems Neuroscience

    Background:

    • The nervous system's complexity presents significant experimental and theoretical challenges.
    • Existing research provides broad physiological and anatomical insights but lacks a comprehensive understanding of neural information processing.
    • A gap exists in explaining how the nervous system acquires and processes information.

    Purpose of the Study:

    • To review neural models that can be experimentally tested.
    • To integrate diverse neurophysiological findings into unified theories.
    • To investigate information-processing mechanisms, codes, and networks within neural systems.

    Main Methods:

    • Focus on neural models testable via physiological or psychophysical experiments.

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  • Emphasis on identifying model parameters from experimental measurements.
  • Overview of neural firing models, interaction models, and coding strategies.

    • Main Results:

    • Discussion of neural firing and interaction models.
    • Exploration of neural variability and coding mechanisms.
    • Integration of experimental data with theoretical models.

    Conclusions:

    • Neural modeling provides a quantitative framework for understanding complex nervous system functions.
    • Testable models are crucial for bridging experimental data and theoretical understanding.
    • This review highlights the importance of neural modeling in advancing neuroscience.