Visual perception of liquids: insights from deep neural networks

Datasets and analysis code of the following publication: Van Assen, J.J.R., Nishida, S. &amp; Fleming, R. W. (2020). Visual perception of liquids: insights from deep neural networks. <em>PLOS Computational Biology. </em>DOI: 10.1371/journal.pcbi.1008018 For any questions please contact the first author at mail [at] janjaap [dot] info <strong>Contents:</strong> 1. DataAnalysis<br> - Jupyter Notebook to run the full analysis in R<br> - For installation details see: https://irkernel.github.io/requirements/ 2. FullStimulusSet<br> - 2 million liquid images with 16 viscosities, 10 scenes, 625 variations, and 20 frames<br> - Matlab script that merges the images horizontally for network input 3. NeuralActivations<br> - Matlab files containing the neural activations if you cannot read out the networks 4. TrainedNetworks<br> - 100 Trained networks referred to in the paper using Matlab and the Deep Learning Toolbox<br> - One custom layer file “switchLayerAdvanced.m” 5. ValidationSet<br> - 800 experimental stimuli that were used for validation 16 viscosities, 10 scenes, 5 variations (1,6,11,16,21)<br> - Matlab script that merges the images horizontally for network input