Reinforcement Learning in a Spiking Neural Model of Striatum Plasticity

This repository hosts the simulation code and related materials for the scientific article:

González-Redondo, Á., Garrido, J., Arrabal, F. N., Kotaleski, J. H., Grillner, S., & Ros, E. (2023). Reinforcement learning in a spiking neural model of striatum plasticity. Neurocomputing, 548, 126377.

The primary focus is on a spiking neural model that implements reinforcement learning mechanisms in the context of striatum plasticity.

Corresponding author and maintainer: Álvaro González-Redondo (alvarogr@ugr.es).

Compilation instructions

1. Navigate to the edlut_lib/compiled directory:

   • Use the command: cd edlut_lib/compiled

2. Run CMake to configure the project and prepare the build process:

   • Use the command: cmake .. -Dwith-python=3
   • Ensure you have CMake installed and Python 3 is available on your system.

3. Compile the project:

   • Use the command: make
   • Make sure you have the necessary compilers and dependencies installed.

4. Return to the root folder of the project:

   • Use the command: cd ../..

5. Create a symbolic link to the compiled Python library in the root folder:

   • Use the command: ln -s edlut_lib/compiled/python/ edlut
   • This step is essential for Python to find the compiled modules.

6. Run the Jupyter Notebook:

   • Make sure Jupyter Notebook is installed.
   • Open the notebook 01 - Network generalized single run.ipynb using Jupyter Notebook.
   • Execute the notebook cells to run your code.