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Related Experiment Videos

Intrinsic gain modulation and adaptive neural coding.

Sungho Hong1, Brian Nils Lundstrom, Adrienne L Fairhall

  • 1Physiology and Biophysics Department, University of Washington, Seattle, Washington, USA. shhong@oist.jp

Plos Computational Biology
|July 19, 2008
PubMed
Summary
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Neural computations use linear/nonlinear models where filters and gain curves adapt to input variance. This study links sensory adaptation and neural gain control by background noise, revealing intrinsic nonlinearity

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neural computation is often modeled using linear/nonlinear (LN) models, comprising linear filters and nonlinear gain curves.
  • Sensory adaptation involves rapid adjustments in these LN model components in response to input variance.
  • Background noise in cortical neurons influences firing rate (f-I) curve gain, a phenomenon termed gain control.

Purpose of the Study:

  • To establish a direct correspondence between sensory adaptation and neural gain control by background noise.
  • To elucidate the role of intrinsic nonlinearity in variance-dependent gain modulation and adaptive neural computation.

Main Methods:

  • Relating variance-dependent gain changes in f-I curves to empirical LN models derived from sampling.

Related Experiment Videos

  • Deriving relationships between gain changes (mean and variance) and receptive fields from white noise stimuli.
  • Utilizing two conductance-based model neurons with distinct gain modulation properties.

    • Main Results:

    • Demonstrated a quantitative link between variance-dependent gain modulation and the characteristics of the changing LN model.
    • Showed that model neurons with varying parameters quantitatively satisfy derived relationships.
    • Identified intrinsic nonlinearity as the underlying mechanism for both phenomena.

    Conclusions:

    • Variance-dependent gain modulation in neural systems is directly related to adaptive changes in linear filters and gain curves.
    • The study provides a unified framework for understanding adaptive neural computation and gain control.
    • Intrinsic nonlinearity is a fundamental factor driving these dynamic neural processes.