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Natural signal statistics and sensory gain control

Abstract

We describe a form of nonlinear decomposition that is well-suited for efficient encoding of natural signals. Signals are initially decomposed using a bank of linear filters. Each filter response is then rectified and divided by a weighted sum of rectified responses of neighboring filters. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, provides a good characterization of the nonlinear response properties of typical neurons in primary visual cortex or auditory nerve, respectively. These results suggest that nonlinear response properties of sensory neurons are not an accident of biological implementation, but have an important functional role.

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Figure 1: Linear filter responses to example image and sound stimuli.
Figure 2: Joint statistics of a typical natural image as seen through two linear filters.
Figure 3: Examples of variance dependency in natural signals.
Figure 4: Generic normalization model for vision and audition.
Figure 5: Classical nonlinear behaviors of V1 neurons.
Figure 6: Suppression of responses to optimal stimuli by masking stimuli.
Figure 7: Nonlinear changes in tuning curves at different input levels.

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Acknowledgements

We thank J. Cavanaugh, W. Bair, and J.A. Movshon for providing us with physiological data.

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Correspondence to Eero P. Simoncelli.

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Schwartz, O., Simoncelli, E. Natural signal statistics and sensory gain control. Nat Neurosci 4, 819–825 (2001). https://doi.org/10.1038/90526

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