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Journal Article

Tailored ensembles of neural networks optimize sensitivity to stimulus statistics

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Zierenberg,  Johannes
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Wilting,  Jens
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173619

Priesemann,  Viola
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0009-CFFE-1
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.