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Efficient coding of natural scenes improves neural system identification

Yongrong Qiu, David A. Klindt, Klaudia P. Szatko, Dominic Gonschorek, Larissa Hoefling, Timm Schubert, Laura Busse, Matthias Bethge, View ORCID ProfileThomas Euler
doi: https://doi.org/10.1101/2022.01.10.475663
Yongrong Qiu
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
cGraduate Training Centre of Neuroscience (GTC), International Max Planck Research School, U Tübingen, 72076 Tübingen, Germany
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David A. Klindt
dDepartment of Mathematical Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Klaudia P. Szatko
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
cGraduate Training Centre of Neuroscience (GTC), International Max Planck Research School, U Tübingen, 72076 Tübingen, Germany
eBernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
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Dominic Gonschorek
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
fResearch Training Group 2381, U Tübingen, 72076 Tübingen, Germany
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Larissa Hoefling
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
eBernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
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Timm Schubert
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
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Laura Busse
gDivision of Neurobiology, Faculty of Biology, LMU Munich, 82152 Planegg-Martinsried, Germany
hBernstein Centre for Computational Neuroscience, 82152 Planegg-Martinsried, Germany
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Matthias Bethge
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
eBernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
iInstitute for Theoretical Physics, U Tübingen, 72076 Tübingen, Germany
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Thomas Euler
aInstitute for Ophthalmic Research, U Tübingen, 72076 Tübingen, Germany
bCentre for Integrative Neuroscience (CIN), U Tübingen, 72076 Tübingen, Germany
eBernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
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  • ORCID record for Thomas Euler
  • For correspondence: thomas.euler@cin.uni-tuebingen.de
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Abstract

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the “stand-alone” system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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-ND 4.0 International license.
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Posted April 29, 2022.
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Efficient coding of natural scenes improves neural system identification
Yongrong Qiu, David A. Klindt, Klaudia P. Szatko, Dominic Gonschorek, Larissa Hoefling, Timm Schubert, Laura Busse, Matthias Bethge, Thomas Euler
bioRxiv 2022.01.10.475663; doi: https://doi.org/10.1101/2022.01.10.475663
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Efficient coding of natural scenes improves neural system identification
Yongrong Qiu, David A. Klindt, Klaudia P. Szatko, Dominic Gonschorek, Larissa Hoefling, Timm Schubert, Laura Busse, Matthias Bethge, Thomas Euler
bioRxiv 2022.01.10.475663; doi: https://doi.org/10.1101/2022.01.10.475663

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