Computational investigation of interplay between the low-dimensional dynamics of a recurrent neural network and plastic recurrent weight changes.
Corresponding paper: Feulner and Clopath, 2021.
Requires Python 3.
python 3.9.12 and pip 21.2.4
# create python virtual environment
python -m venv manifold_env
# activate environment
source manifold_env/bin/activate
# install dependencies
pip install -r requirements.txt
Move to the clone directory (e.g., in my home directory):
cd ~/neural-manifold-and-plasticity/
Run simulation (takes 10 min):
python fig2_simulation.py
The script creates simulation files stored in data/fig2/:
- data/
fig2/
experiment_results.npy
network.npz
relearning_results.npy
W_initial.npy
W_outside.npy
W_stabilized.npy
W_within.npy
Reproduce figure 2 (.svg files stored in figures/fig2/):
python fig2_plots.py
run simulation figure 3 (takes 10 min):
python fig3_simulation.py
Reproduce figure 3 (.svg files stored in figures/fig4/):
python fig3_plots.py
Run simulation for figure 4 (takes 10 min):
python fig4_simulation.py
Reproduce figure 4 (.svg files stored in figures/fig4/):
python fig4_plots.py
etc ...