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      Contrast Gain Control in Auditory Cortex

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          Summary

          The auditory system must represent sounds with a wide range of statistical properties. One important property is the spectrotemporal contrast in the acoustic environment: the variation in sound pressure in each frequency band, relative to the mean pressure. We show that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation. When contrast is low, neurons increase their gain, becoming more sensitive to small changes in the stimulus, although the effectiveness of contrast gain control is reduced at low mean levels. Gain is primarily determined by contrast near each neuron's preferred frequency, but there is also a contribution from contrast in more distant frequency bands. Neural responses are modulated by contrast over timescales of ∼100 ms. By using contrast gain control to expand or compress the representation of its inputs, the auditory system may be seeking an efficient coding of natural sounds.

          Highlights

          ► We find evidence for spectrotemporal contrast gain control in auditory cortex ► Gain is determined by a combination of spectrally local and global contrast ► Within a limited range, mean stimulus level also affects neural gain ► Contrast gain control is fast (∼100 ms); gain decreases are faster than increases

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          Most cited references56

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          Some informational aspects of visual perception.

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            Normalization of cell responses in cat striate cortex.

            D. Heeger (1992)
            Simple cells in the striate cortex have been depicted as half-wave-rectified linear operators. Complex cells have been depicted as energy mechanisms, constructed from the squared sum of the outputs of quadrature pairs of linear operators. However, the linear/energy model falls short of a complete explanation of striate cell responses. In this paper, a modified version of the linear/energy model is presented in which striate cells mutually inhibit one another, effectively normalizing their responses with respect to stimulus contrast. This paper reviews experimental measurements of striate cell responses, and shows that the new model explains a significantly larger body of physiological data.
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              A simple white noise analysis of neuronal light responses.

              A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.
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                Author and article information

                Journal
                Neuron
                Neuron
                Neuron
                Cell Press
                0896-6273
                1097-4199
                23 June 2011
                23 June 2011
                : 70
                : 6
                : 1178-1191
                Affiliations
                [1 ]Department of Physiology, Anatomy, and Genetics, Sherrington Building, Parks Road, University of Oxford, Oxford OX1 3PT, UK
                Author notes
                []Corresponding author neil.rabinowitz@ 123456merton.ox.ac.uk
                [∗∗ ]Corresponding author andrew.king@ 123456dpag.ox.ac.uk
                [2]

                These authors contributed equally to this work

                Article
                NEURON10703
                10.1016/j.neuron.2011.04.030
                3133688
                21689603
                72b77f4a-4db9-4c93-83d7-04b16a23848e
                © 2011 ELL & Excerpta Medica.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 21 April 2011
                Categories
                Article

                Neurosciences
                Neurosciences

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