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Interactive Topographic Network modeling framework

Description

• Code for training and evaluating Interactive Topographic Network models of visual cortical topography

Citation

@article{Blauch2022Connectivity,
  title={A connectivity-constrained computational account of topographic organization in primate high-level visual cortex},
  author={Nicholas M. Blauch, Marlene Behrmann, David C. Plaut},
  journal={Proceedings of the National Academy of Sciences},
  year={2022},
  issue={199 (3)},
}

Setup

• Setup an appropriate virtual environment on a CUDA-ready machine using requirements.txt. with conda:

conda create --name topographic python=3.6.10
conda activate topographic
conda install cudatoolkit=10.1 -c pytorch # if on linux or otherwise needed
pip install -r requirements.txt
# optionally pip install the topographic module 
python setup.py install develop

• modify your .bashrc file to set the environment variable SERVER_NAME. and then configure directories as appropriate using toographic/config.py

export SERVERNAME='nick-local'

• Configure use of SLURM for job submission if planning to train models (local use is also possible). Other HPC systems are possible but will require separate configuration.

• If using SLURM, edit the file run_slurm.sbatch to adjust for your particular setup.

• Acquire ImageNet, VGGFace2, and Places365 from common web sources

Pre-train encoders

. scripts/train_encoders.sh

Train ITN models

. scripts/train_all_itns.sh

Data on KiltHub

• Alternatively to running the models yourself, you can download all of the pre-trained and fine-tuned models along with relevant results necessary for all plots in the paper on Kilthub: https://doi.org/10.1184/R1/17131319

Plot the results

View the jupyter notebooks; we recommend using Jupyter Lab (which can be installed and launched with:)

conda install -c conda-forge jupyterlab
jupyter-lab

You must have your paths configured and results produced, either through running the experiments yourself, or downloading the KiltHub data and configuring topographic.config.DATA_DIR to point to the directory holding its data.

notebooks/main_model.ipynb shows how to visualize in depth results of the main model and other variants notebooks/variants.ipynb shows how to compare ITN variants, with different architectures and wiring penalties ($\lambda_w$)

questions and bugs:

create an issue. if a bug, please describe in detail what you have tried and any steps necessary to reproduce the issue.