Supplementary information for: H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

Here we give details to our models, and to the encoding and representation of images and questions used in our models. We used a CNN as input encoder in this task. The last fully connected layer was of size 128 followed by a ReLU nonlinearity (BatchNorm denotes a batch normalization layer [2]). Training examples were generated as described in the main text. Here, an example is one full sequence of image pairs (including random images) and one query image.