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  Evaluating neuronal codes for inference using Fisher information

Haefner, R., & Bethge, M. (2010). Evaluating neuronal codes for inference using Fisher information. Frontiers in Computational Neuroscience, 2010(Conference Abstract: Bernstein Conference on Computational Neuroscience). doi:10.3389/conf.fncom.2010.51.00069.

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Haefner, R1, 2, Author           
Bethge, M1, 2, Author           
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1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Many studies have explored the impact of response variability on the quality of sensory codes. The source of this variability is almost always assumed to be intrinsic to the brain. However, when inferring a particular stimulus property, variability associated with other stimulus attributes also effectively acts as noise. Here we explore the impact of such stimulus-induced response variability for two model cases: a) binocular disparity inference and b) orientation discrimination. We characterize the response distribution for the energy model in response to random dot stereograms and to displays of oriented random dots.For the case of bincular disparity processing we find the response distribution to be very different from the Poisson-like noise usually assumed. We compute the Fisher information with respect to binocular disparity, present in the monocular inputs to the standard model of early binocular processing, and thereby obtain an upper bound on how much information a model could theoretically extract from them. Then we analyze the information loss incurred by the different ways of combining those inputs to produce a scalar single-neuron response. We find that in the case of depth inference, monocular stimulus variability places a greater limit on the extractable information than intrinsic neuronal noise for typical spike counts. Furthermore, the largest loss of information is incurred by the standard model for position disparity neurons (tuned-excitatory), that are the most ubiquitous in monkey primary visual cortex, while more information from the inputs is preserved in phase-disparity neurons (tuned-near or tuned-far) primarily found in higher cortical regions.

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 Dates: 2010-09
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/conf.fncom.2010.51.00069
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Title: Bernstein Conference on Computational Neuroscience (BCCN 2010)
Place of Event: Berlin, Germany
Start-/End Date: 2010-09-27 - 2010-10-01

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Title: Frontiers in Computational Neuroscience
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 2010 (Conference Abstract: Bernstein Conference on Computational Neuroscience) Sequence Number: - Start / End Page: - Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188