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Nonlinear decoding of a complex movie from the mammalian retina




Author summary Neurons in the retina transform patterns of incoming light into sequences of neural spikes. We recorded from ∼100 neurons in the rat retina while it was stimulated with a complex movie. Using machine learning regression methods, we fit decoders to reconstruct the movie shown from the retinal output. We demonstrated that retinal code can only be read out with a low error if decoders make use of correlations between successive spikes emitted by individual neurons. These correlations can be used to ignore spontaneous spiking that would, otherwise, cause even the best linear decoders to “hallucinate” nonexistent stimuli. This work represents the first high resolution single-trial full movie reconstruction and suggests a new paradigm for separating spontaneous from stimulus-driven neural activity.

Author(s): Vicente Botella-Soler and Stéphane Deny and Georg Martius and Olivier Marre and Gašper Tkačik
Journal: PLOS Computational Biology
Volume: 14
Number (issue): 5
Pages: 1-27
Year: 2018
Month: May
Publisher: Public Library of Science

Department(s): Autonomous Learning
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1371/journal.pcbi.1006057


  title = {Nonlinear decoding of a complex movie from the mammalian retina},
  author = {Botella-Soler, Vicente and Deny, Stéphane and Martius, Georg and Marre, Olivier and Tkačik, Gašper},
  journal = {PLOS Computational Biology},
  volume = {14},
  number = {5},
  pages = {1-27},
  publisher = {Public Library of Science},
  month = may,
  year = {2018},
  month_numeric = {5}