This repository contains code to accompany the paper
Duncker, L.*, Driscoll, L.*, Shenoy, K. V., Sahani, M.**, & Sussillo, D.** (2020). Organizing recurrent network dynamics by task-computation to enable continual learning. Advances in Neural Information Processing Systems, 33.
@inproceedings{duncker+driscoll:2020:neurips,
title={Organizing recurrent network dynamics by task-computation to enable continual learning},
author={Duncker, Lea and Driscoll, Laura N and Shenoy, Krishna V and Sahani, Maneesh and Sussillo, David},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
the repository is based on work in
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X. J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature neuroscience, 22(2), 297-306.
and code which can be found here (though versions might have diverged)
The code runs on Python 2.7 and an older tensorflow version. After cloning the repository, you can create a virtual environment and install the requirements using
virtualenv -p /usr/bin/python2.7 seqRNN
source seqRNN/bin/activate
pip install -r requirements-cpu.txt
Alternatively, to create an environment with conda:
conda env create -f environment-cpu.yml
conda activate seqRNN
Note that this installs tensorflow to run on your CPU. If you'd rather run tensorflow on a GPU, use requirements-gpu.txt or environment-gpu.yml instead.
An example script for sequentially training an RNN on the task-set from the paper using our continual learning approach is provided in the script example_sequential_training.py
The folder data/trained_models/ contains an example trained network. analyses/demos.ipynb contains some examples to reproduce analyses from the paper.
Some of the analyses rely on running FixedPointFinder on the trained RNN.
You need to install FixedPointFinder and then specify the relevant Python path in analyses/run_fixed_point_finder.py by editing the line
PATH_TO_FIXED_POINT_FINDER = '/path/to/your/directory/fixed-point-finder/'
to match the path corresponding to your local directory.
