Visual perception of liquids: insights from deep neural networks

<p>Datasets and analysis code&nbsp;of the following publication:</p> <p>Van Assen, J.J.R., Nishida, S.&nbsp;&amp; Fleming, R. W. (2020). Visual perception of liquids: insights from deep neural networks.&nbsp;<em>PLOS Computational&nbsp;Biology.&nbsp;</em>DOI: 10.1371/journal.pcbi.1008018</p> <p>For any questions please contact the first author at mail [at] janjaap [dot] info</p> <p><strong>Contents:</strong></p> <p>1. DataAnalysis<br> - Jupyter Notebook to run the full analysis in R<br> - For installation details see: https://irkernel.github.io/requirements/</p> <p>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</p> <p>3. NeuralActivations<br> - Matlab files containing the neural activations if you cannot read out the networks</p> <p>4. TrainedNetworks<br> - 100 Trained networks referred to in the paper using Matlab and the Deep Learning Toolbox<br> - One custom layer file &ldquo;switchLayerAdvanced.m&rdquo;</p> <p>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</p>