Official implementation for the paper:

Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs

Installation

Clone the repo:

git clone https://github.com/yuvenduan/PlasticRNNs
cd PlasticRNNs

Install dependencies in a new conda environment:

conda env create -f environment.yml
conda activate plastic

If you are interested in reproducing results on one-shot image classification, download CIFAR-FS and/or miniImageNet. Unzip the datasets and put the files under data/CIFAR_FS and data/miniImageNet. Alternatively, you can change the path to the datasets in datasets/fsc/CIFAR_FS.py and datasets/fsc/mini_imagenet.py.

Usage

As a demo for the cue-reward association task, run:

python main.py -t cuereward_demo

If you have multiple GPUs on your machine, you can add -s so that experiments could be run concurrently. If you are using a slurm cluster, you can add -c so that experiments will be submitted to the cluster.

During training, progress can be found in experiments/cuereward_demo/.../progress.txt. After training, try

python main.py -a cuereward_demo

to plot the training curves. The result is stored as figures/cuereward/curves-demo.pdf.

Configurations for all experiments in the paper can be found at configs/experiments.py, whereas the analyses can be found at configs/exp_analysis.py. These experiments and analyses can be run similarly.

Acknowledgments

The codebase is based on the code from Meta-Learning with Differentiable Convex Optimization and Evolving the olfactory system with machine learning.