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Model neuron response statistics to natural images

Arvind V Iyer, Johannes Burge
doi: https://doi.org/10.1101/387183
Arvind V Iyer
University of Pennsylvania
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  • For correspondence: arvindiy@sas.upenn.edu
Johannes Burge
University of Pennsylvania
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Abstract

To model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise impact the model neuron responses to natural images and the signal-to-noise ratio for natural image discrimination. We show that when these components are modeled appropriately, the model neuron responses to natural stimuli are Gaussian distributed, scale-invariant, and very nearly maximize the signal-to-noise ratio for stimulus discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus- and feature-specific) normalization causes model neurons to yield Gaussian-distributed responses to natural stimuli, 1/f noise stimuli, and white noise stimuli. The current work makes recommendations for best practices and it lays a foundation, grounded in the response statistics to natural stimuli, upon which principled models of more complex visual tasks can be built.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 07, 2018.
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Model neuron response statistics to natural images
Arvind V Iyer, Johannes Burge
bioRxiv 387183; doi: https://doi.org/10.1101/387183
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Model neuron response statistics to natural images
Arvind V Iyer, Johannes Burge
bioRxiv 387183; doi: https://doi.org/10.1101/387183

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