Contrastive-Signal-Dependent-Plasticity (CSDP)

This is the code for the paper: "Contrastive-Signal-Dependent-Plasticity: Self-Supervised Learning in Spiking Neural Circuits" a preprint of which can be found here: https://arxiv.org/abs/2303.18187
Note that this code assumes that you have Python 3.11, jax/jaxlib 0.4.26 (for Cuda-12), and ngclearn 1.0.b3 (with ngcsimlib 0.2.b2) successfully installed on your system.

Make sure you unzip the mnist data prepared for you in the /data/ folder (unzip /data/mnist.zip and place it inside of /data/). To train a CSDP SNN model (with 1024 neuronal cells in each layer), run the following prepared BASH script:

./sim_csdp.sh

This will train a CSDP SNN model on the MNIST database for you. Furthermore, the script will generate the model structure (in ngc-learn JSON format) as well as store NPZ files containing your best found parameters during training. All of this will be stored, if you run the script in its default mode (i.e., w/o modifying its arguments) to a folder exp_supervised_mnist/ which contains your saved ngc-learn CSDP SNN model.

To evaluate your CSDP model after training it, run the following analysis script

python analyze_csdp.py  --dataX=data/mnist/testX.npy \
                        --dataY=data/mnist/testY.npy \
                        --modelDir=exp_supervised_mnist/ \
                        --paramDir=best_params1234

For the analysis script, the parameter sub-directory can be toggled by changing the "paramDir" argument, which is simply the name of the sub-directory within your model directory "modelDir" that contains the saved NPZ synaptic arrays. Inside the output directory it creates /exp/, you will find a t-SNE plot of your model's extracted latent codes.