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Comparing models of contrast gain using psychophysical experiments.

Christopher DiMattina

    Journal of Vision
    |July 6, 2016
    PubMed
    Summary

    This study shows how psychophysical experiments can compare neural models of multiplicative gain modulation. Biologically interpretable models fitted to behavioral data accurately estimate neural tuning parameters.

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Visual Neuroscience

    Background:

    • Neural responses are often multiplicatively modulated by stimulus features like intensity or contrast.
    • Understanding contrast gain modulation in visual neurons is crucial for visual processing.
    • Existing methods may not effectively distinguish between similar neural coding hypotheses.

    Purpose of the Study:

    • To demonstrate a psychophysical methodology for comparing competing hypotheses of multiplicative gain modulation.
    • To apply this method to contrast gain modulation in orientation-tuned visual neurons.
    • To validate the use of biologically interpretable models for estimating neural parameters from behavioral data.

    Main Methods:

    • Utilizing psychophysical experiments with adaptively generated stimuli.

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  • Fitting biologically interpretable computational models to human behavioral data.
  • Comparing model fits to distinguish between different hypotheses of neural gain modulation.

    • Main Results:

    • Psychophysical data, when analyzed with appropriate models, can yield physiologically accurate estimates of neural contrast tuning parameters.
    • The proposed methodology effectively distinguishes between qualitatively similar hypotheses of contrast gain modulation.
    • Adaptive stimulus generation enhances the power of psychophysical methods for model comparison.

    Conclusions:

    • Psychophysical experiments combined with biologically interpretable models offer a powerful approach to understanding neural coding.
    • This methodology provides a robust framework for comparing competing neural models in various sensory systems.
    • Future research can extend this approach to investigate other forms of neural modulation using behavioral data.