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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, pp. 1-9. doi:10.1103/PhysRevResearch.2.013115.

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Zierenberg, J, Author
Wilting, J, Author
Priesemann, V, Author
Levina, A1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain. For a generic, tunable spiking network we derive that while the dynamic range is maximal at criticality, the interval of discriminable intensities is very similar for any network tuning due to coalescence. Compensating coalescence enables adaptation of discriminable intervals. Thus, we can tailor an ensemble of networks optimized to the distribution of stimulus intensities, e.g., extending the dynamic range arbitrarily. We discuss potential applications in machine learning.

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 Dates: 2019-052019-122020-02
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1103/PhysRevResearch.2.013115
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Title: Physical Review Research
Source Genre: Journal
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Publ. Info: College Park, Maryland, United States : American Physical Society (APS)
Pages: - Volume / Issue: 2 Sequence Number: 013115 Start / End Page: 1 - 9 Identifier: ISSN: 2643-1564
CoNE: https://pure.mpg.de/cone/journals/resource/2643-1564