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  Tailored ensembles of neural networks optimize sensitivity to stimulus statistics

Zierenberg, J., Wilting, J., Priesemann, V., & Levina, A. (2020). Tailored ensembles of neural networks optimize sensitivity to stimulus statistics. Physical Review Research, 2: 013115. doi:10.1103/PhysRevResearch.2.013115.

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 Creators:
Zierenberg, Johannes1, Author           
Wilting, Jens1, Author           
Priesemann, Viola1, Author           
Levina, Anna, Author
Affiliations:
1Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2616694              

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 Abstract: The capability of a living organism to process stimuli with nontrivial intensity distributions cannot be
explained by the proficiency of a single neural network. Moreover, it is not sufficient to maximize the dynamic
range of the neural response; it is also necessary to tune the response to the intervals of stimulus intensities that
should be reliably discriminated. We derive a class of neural networks where these intervals can be tuned to
the desired interval. This allows us to tailor ensembles of networks optimized for arbitrary stimulus intensity
distributions. We discuss potential applications in machine learning.

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 Dates: 2020-02-032020
 Publication Status: Issued
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 Identifiers: DOI: 10.1103/PhysRevResearch.2.013115
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Title: Physical Review Research
Source Genre: Journal
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Pages: 9 Volume / Issue: 2 Sequence Number: 013115 Start / End Page: - Identifier: ISSN: 2643-1564