Data for Cadena et al. 2019, PlosCB

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README.md

Data for Cadena et al. 2019 PlosCB

Data for the PlosCB 2019 paper Deep convolutional models improve predictions of macaque V1 responses to natural images

The file data_binned_responses/cadena_ploscb_data.pkl contains the stimuli, extracted neuron spike counts, and metadata of Cadena et al., 2019 PlosCB. For more details about the data check the paper.

Loading the data

To load the data you need python and the pickle library

import pickle
with open('binned_responses/cadena_ploscb_data.pkl', 'rb') as g:
    loaded_data = pickle.load(g)

Check contents of data file

[(i,loaded_data[i].shape) for i in loaded_data]

[('repetitions', (166,)), ('subject_id', (166,)), ('session_id', (166,)), ('image_ids', (7250, 1)), ('unit_id', (166,)), ('image_types', (7250, 1)), ('images', (7250, 140, 140)), ('responses', (4, 7250, 166)), ('image_numbers', (7250, 1))]

Dictionary fields

  • images: The stimuli used in the paper. Array of shape (num_images, width, height)
  • responses: The extracted spike count responses to those images in the same order. Array of shape (num_repetitions, num_images, num_neurons). Aborted trials and fewer repetitions for some sessions resulted in NaNs in this array.
  • image_types: Stimulus types of each image: 'original', 'conv1', conv2', conv3, or conv4. See paper for details.
  • unit_id: identifier of the neurons
  • image_id: identifier of the images
  • session_id: identifier of the session for each neuron
  • subject_id: identifier of the monkey for each neuron
  • repetitions: Number of repeates of the entire stimulus during the session for each neuron.

This data is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This license requires that you contact us before you use the data in your own research. In particular, this means that you have to ask for permission if you intend to publish a new analysis performed with this data (no derivative works-clause).

Datacite.yml
Title Data for Cadena et al. 2019 Plos Computational Biology
Authors Cadena,Santiago;University of Tuebingen;https://orcid.org/0000-0002-7508-4443
Denfield,George;Baylor College of Medicine
Walker,Edgar;Baylor College of Medicine
Gatys,Leon;University of Tuebingen
Tolias,Andreas;Baylor College of Medicine
Bethge,Matthias;University of Tuebingen
Ecker,Alexander;University of Tuebingen
Description Macaque V1 single neural responses to naturalistic images recorded with silicon laminar probes.
License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
References Deep convolutional models improve predictions of macaque V1 responses to natural images [] (IsPartOf)
Funding DFG, EC 479/1-1
SFB 1233, Robust Vision
FKZ, 01GQ1002
EXC307
R01EY026927
DP1-EY023176
DP1-OD008301
EY-002520-37
NEI T32EY00700140
NEI F30EY025510
DoI D16PC00003
Keywords Neuroscience
Electrophysiology
Awake macaques
Primary visual cortex
Natural stimulation
Resource Type Dataset