****Link to the paper at Nature Human Behvaior: https://www.nature.com/articles/s41562-021-01264-9
-MNIST VAE was adopted from http://github.com/lyeoni/pytorch-mnist-VAE/blob/master/pytorch-mnist-VAE.ipynb
MLR is a model of working memory coupled with visual knowledge
-The visual knowledge represented by mVAE is trained on MNIST and f-MNIST -The skip connection (from L1 to L5) was trained on cropped MNIST and f-MNIST that were presented in different locations with some degrees of rotation -tokens_capacity.py file consists of functions that compute cross correlations for novel vs. familiar shapes , detect whether a given stimulus is novel or familiar and do the binding test -.png images are the novel images used in the model to assess memory for novel shapes -the model and classifiers are defined in mVAE.py file
To train the model: Run the Training.py and save the model in output1 (you can run 10 times and save them in different directories from output1 to output10)
To train the classifiers:
Run the Training_classifier.py. This trains and saves the classifiers trained on each model (output1 to output10)
To get the figures in the manuscript:
run the plot.py file. Within the file there are flags that can be set to 1 or zero corresponding to the figures/tables in the manuscript
To get the pre-trained model in addition to the simulation results please visit the OSF webpage (https://osf.io/tpzqk/). Run the plots.py file to reproduce the results
Package Requirements are as listed in requirements.txt