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README.md 76ab6ea9e6 Update 'decoding scans/README.md' 1 year ago

README.md

Dataset Structure

The decoding scans are provided as .h5 files. There are two types of decoding scans:

  • Objects: For the object discrimation task
  • Ellipse: For the detection task

The following attributes are present in each individual dataset:

  • pupil_trials: mean pupil size for each trial
    • shape:n_trials,
  • class_trials: image class for each trial. See below for details.
    • shape:n_trials,
  • activity_trials: mean activity per neuron for each trial
    • shape:n_neurons x n_trials

class_trials description

Object discrimination task

Paradigm 1
  • o1BLfgv2: object 1, green channel, foreground (Fig. 5)
  • o2BLfgv2: object 2, green channel, foreground (Fig. 5)
  • o1UVfgv2: object 1, UV channel, foreground (Fig. 5)
  • o2UVfgv2: object 2, UV channel, foreground (Fig. 5)
Paradigm 2
  • o1BLfbv2: object 1, green channel, foreground & background & positive contrast (Extended Data Fig. 10)
  • o2BLfbv2: object 2, green channel, foreground & background & positive contrast (Extended Data Fig. 10)
  • o1UVfbv2: object 1, UV channel, foreground & background & positive contrast (Extended Data Fig. 10)
  • o2UVfbv2: object 2, UV channel, foreground & background & positive contrast (Extended Data Fig. 10)
Paradigm 3
  • o1BLfdv2: object 1, green channel, foreground & background & negative contrast (Extended Data Fig. 10)
  • o2BLfdv2: object 2, green channel, foreground & background & negative contrast (Extended Data Fig. 10)
  • o1UVfdv2: object 1, UV channel, foreground & background & negative contrast (Extended Data Fig. 10)
  • o2UVfdv2: object 2, UV channel, foreground & background & negative contrast (Extended Data Fig. 10)
Paradigm 4
  • o1BLnbv2: object 1, green channel, no background/foreground (Extended Data Fig. 10)
  • o2BLnbv2: object 2, green channel, no background/foreground (Extended Data Fig. 10)
  • o1UVnbv2: object 1, UV channel, no background/foreground (Extended Data Fig. 10)
  • o2UVnbv2: object 2, UV channel, no background/foreground (Extended Data Fig. 10)

Detection task

  • green: dark ellipse is present in the green color channel
  • uv: dark ellipse is present in the UV color channel
  • noise: no ellipse is present
datacite.yml
Title State-dependent pupil dilation rapidly shifts visual feature representations
Authors Franke,Katrin;Institute for Ophthalmic Research, Tuebingen University, Tuebingen, Germany.
Willeke,Konstantin F.;Institute for Bioinformatics and Medical Informatics, Tuebingen University, Tuebingen, Germany
Ponder,Kayla;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Galdamez,Mario;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Zhou,Na;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Muhammad,Taliah;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Patel,Saumil;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Froudarakis,Emmanouil;Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
Reimer,Jacob;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Sinz,Fabian;Department of Computer Science, Goettingen University, Goettingen, Germany
Tolias,Andreas;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Description Complete dataset for the article "State-dependent pupil dilation rapidly shifts visual feature representations".
License CC-BY (http://creativecommons.org/licenses/by/4.0/)
References Franke, K., Willeke, K.F., Ponder, K., Galdamez, M., Muhammad, T., Patel, S., Froudarakis, E., Reimer, J., Sinz, F., & Tolias, A.S. (2021). Behavioral state tunes mouse vision to ethological features through pupil dilation. bioRxiv. [doi:10.1101/2021.09.03.458870] (IsSupplementTo)
Funding DFG, EXC 2064/1
IARPA, D16PC00003
NIH, R01 EY026927
NIH, T32-EY-002520-37
NSF, 1707400
Carl-Zeiss-Stiftung
Keywords Neuroscience
Systems Neuroscience
Primary visual cortex
Brain state
Behavioral state
Machine learning
Deep neural networks
Resource Type Dataset