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Sparse coding predicts optic flow specificities of zebrafish pretectal neurons

  • Gerrit A. EckeEmail author
  • Sebastian A. Bruijns
  • Johannes Hölscher
  • Fabian A. Mikulasch
  • Thede Witschel
  • Aristides B. Arrenberg
  • Hanspeter A. Mallot
Brain inspired Computing&Machine Learning Applied Research-BISMLARE
  • 30k Downloads

Abstract

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of three learning networks: a sparse coding network (competitive learning), PCA whitening with subsequent sparse coding, and a backpropagation network (supervised learning). All simulations developed specificities for optic flow which are comparable to those found in a neurophysiological study (Kubo et al. in Neuron 81(6):1344–1359, 2016.  https://doi.org/10.1016/j.neuron.2014.02.043), but relative frequencies of the various neuronal responses were best modeled by the sparse coding approach without whitening. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show not only typical optic flow fields but also “Gabor” and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.

Keywords

Optic flow Sparse coding Optimality pretectum Egomotion detection 

Notes

Acknowledgements

This work was carried out at the Department of Biology of the Eberhard-Karls-University, Tübingen, Germany. Additional support was obtained for TW from the Deutsche Forschungsgemeinschaft within the Werner Reichardt Center for Integrative Neuroscience (CIN), Tübingen.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

Supplementary material

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Supplementary material 1 (pdf 110938 KB)
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Supplementary material 2 (pdf 94286 KB)
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Supplementary material 3 (pdf 97708 KB)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiologyUniversity of TübingenTübingenGermany

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