Show Reference: "Tuning Curves, Neuronal Variability, and Sensory Coding"

Tuning Curves, Neuronal Variability, and Sensory Coding PLoS Biology, Vol. 4, No. 4. (21 March 2006), e92, doi:10.1371/journal.pbio.0040092 by Daniel A. Butts, Mark S. Goldman
    abstract = {Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron's role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron's most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding.},
    address = {Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.},
    author = {Butts, Daniel A. and Goldman, Mark S.},
    citeulike-article-id = {582437},
    citeulike-linkout-0 = {},
    citeulike-linkout-1 = {},
    citeulike-linkout-2 = {},
    citeulike-linkout-3 = {},
    day = {21},
    doi = {10.1371/journal.pbio.0040092},
    issn = {1545-7885},
    journal = {PLoS Biology},
    keywords = {math, neural-coding, noise, tuning-functions},
    month = mar,
    number = {4},
    pages = {e92+},
    pmcid = {PMC1403159},
    pmid = {16529529},
    posted-at = {2014-08-27 11:37:18},
    priority = {2},
    publisher = {Public Library of Science},
    title = {Tuning Curves, Neuronal Variability, and Sensory Coding},
    url = {},
    volume = {4},
    year = {2006}

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Neurons' activities are most informative of the value of stimulus properties in the region where their tuning functions are maximal or have maximal slope.

Which of the two regions is the most informative depends on the variability (noise) of the neurons' responses.

Butts and Goldman use Gaussian functions to model the receptive fields of V1 neurons.