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https://github.com/rylanschaeffer/fietelab-sorscher-2022-reproduction

(Partial) Code for preprint: Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators
https://github.com/rylanschaeffer/fietelab-sorscher-2022-reproduction

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(Partial) Code for preprint: Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators

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README 

        
# Fiete Lab Sorscher 2022 Reproduction



This repository contains (a subset of) our code from our preprint:

[Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators](https://arxiv.org/abs/2312.03954).



![](figures/sorscher_rebuttal.png)



As background, our NeurIPS 2022 paper [No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit](https://proceedings.neurips.cc/paper_files/paper/2022/file/66808849a9f5d8e2d00dbdc844de6333-Paper-Conference.pdf)

was unable to reproduce key findings of Sorscher et al. NeurIPS 2019 [A unified theory for the origin of grid cells through

the lens of pattern formation](https://ganguli-gang.stanford.edu/pdf/19.DecodePattern.pdf).

After the NeurIPS 2022 review process, [code subsequently released](https://github.com/ganguli-lab/grid-pattern-formation/blob/master/square_grid_cells.ipynb)

by Sorscher et al.; however, it was implemented in a deprecated version of TensorFlow (1.14.0),

so we reproduced it here using PyTorch and PyTorch Lightning, then integrated W&Bs to sweep hyperparameters and 

log results for further analysis.



## Setup



Create a conda environment with the required packages:



`conda env create --file environment.yml`



To activate the environment:



`conda activate sorscher_reproduction`



Upgrade pip:



`pip install --upgrade pip`



Then install wandb with pip, recommended by W&B [here](https://docs.wandb.ai/guides/technical-faq/setup).



`pip install wandb`



Then install pytorch lightning:



`pip install lightning`



Then install the correct version of Pytorch:



`pip install torch==1.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html`



I also needed to install an additional system component via conda:



`conda install libgcc`



`pip3 install torch torchvision torchaudio`



## Running



### Running a Single Run



To run a single experiment, run the following command:



`python train_one.py`



The hyperparameters for this run are specified in `src/globals.py`. The results should

automatically be logged to W&B.



### Running via W&B Sweep



To create a sweep, run the following command at the CLI:



`wandb sweep `



This will output the newly created sweep ID, e.g.,:



```

wandb: Creating sweep from: sweeps/20230522/basic.yaml

wandb: Creating sweep with ID: ox6goel1

wandb: View sweep at: https://wandb.ai/rylan/sorscher-2022-reproduction/sweeps/ox6goel1

wandb: Run sweep agent with: wandb agent rylan/sorscher-2022-reproduction/ox6goel1

```



Then create an "Agent" which will pull the next run off the queue and run it to completion, before repeating.

An agent will continue to function until no more runs in the sweep remain. To create an agent, run:



`wandb agent /sorscher-2022-reproduction/`



A sensible way to parallelize is to launch multiple jobs on a cluster, where each job contains 1 agent.

So for instance, if we're running on a SLURM cluster, each SLURM job should create 1 agent.

Rylan has code for this.



## Analysis



For analysis code, see `notebooks/`.



## Contact



Questions? Comments? Interested in collaborating? Open an issue or email Rylan Schaeffer ([email protected])

and cc Mikail Khona ([email protected]) and Ila Fiete ([email protected]).