Merge branch 'release-1.1' into develop

README.md

@@ -1,6 +1,6 @@
 # H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
 This is the code used in the paper "[H-Mem: Harnessing synaptic plasticity with Hebbian Memory
-Networks](https://www.biorxiv.org/content/10.1101/2020.07.01.180372v1)" for training H-Mem on a single-shot
+Networks](https://www.biorxiv.org/content/10.1101/2020.07.01.180372v2)" for training H-Mem on a single-shot
 image association task and on the bAbI question-answering tasks.
 
 ![H-Mem schema](https://i.imgur.com/fK3UWaP.png)
@@ -48,5 +48,21 @@ python babi_task_single.py --task_id=4 --memory_size=20 --epochs=50 --logging=1
 
 will train the model with an associative memory of size 20 on task 4 for 50 epochs. The results will be stored in `results/`.
 
+### Memory-dependent memorization
+In our extended model we have added an 'read-before-write' step. This model will be used if the
+command line argument `--read_before_write` is set to `1`. Run the following command
+
+```bash
+python babi_task_single.py --task_id=16 --epochs=250 --read_before_write=1
+```
+
+to start training on bAbI task 16 in the 10k training examples setting (note that we trained the extended
+model for 250 epochs---instead of 100 epochs). You should get an accuracy of about 100% on this task. Compare
+to the original model, which does not solve task 16, by running the following command
+
+```bash
+python babi_task_single.py --task_id=16 --epochs=250
+```
+
 ## References
-* Limbacher, T., Legenstein, R. (2020). H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks bioRxiv https://www.biorxiv.org/content/10.1101/2020.07.01.180372v1
+* Limbacher, T., Legenstein, R. (2020). H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks bioRxiv https://www.biorxiv.org/content/10.1101/2020.07.01.180372v2