google-deepmind/space_is_a_latent_sequence
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Created: 2025-08-12T22:31:43Z
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space_is_a_latent_sequence
This repository provides the code for the paper, "Space is a latent sequence: A theory of the hippocampus."
It includes the implementation of the paper's underlying model, the CSCG, and example notebooks to reproduce a few results from the paper.
Installation
Create virtual environment and install packages
# git clone repository git clone https://github.com/google-deepmind/space_is_a_latent_sequence.git cd space_is_a_latent_sequence # create virtual environment python3 -m venv venv source venv/bin/activate # install packages pip install -r requirements.txt pip install -e .
On a GPU or TPU machine, install jax for corresponding hardware. See https://jax.readthedocs.io/en/latest/installation.html.
Install jupyter notebook to run the experiment notebooks using the following steps:
# With the virtual env activated, install jupyter using: pip install jupyter # This package is needed to connect your venv to jupyter: pip install ipykernel # Add the venv to jupyter kernels: python -m ipykernel install --user --name=venv # Open jupyter notebook jupyter notebook
Select the venv kernel when running the notebooks.
Usage
The core logic for the CSCG model resides in the cscg folder. Utilities for tasks like visualizing the transition graph or plotting place fields are also included in this folder.
To get started, we recommend exploring the example notebooks in the experiment_notebooks folder. These provide examples of how to train a CSCG and use it for further analysis, such as computing place fields.
Downloading the dataset for the Transitive-learning experiment
To download the random walk dataset for the transitive learning experiment, you will need the gcloud CLI. You can install this by following the instructions here.
To download the dataset, use the following:
gcloud storage cp -r gs://space_is_a_latent_sequence /path/to/data
Citing this work
@article{raju2024space,
title={Space is a latent sequence: A theory of the hippocampus},
author={Raju, Rajkumar Vasudeva and Guntupalli, J Swaroop and Zhou, Guangyao and Wendelken, Carter and L{\'a}zaro-Gredilla, Miguel and George, Dileep},
journal={Science Advances},
volume={10},
number={31},
pages={eadm8470},
year={2024},
publisher={American Association for the Advancement of Science}
}License and disclaimer
Copyright 2024 DeepMind Technologies Limited
All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0
All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode
Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.
This is not an official Google product.
Notability
notability 2.0/10Low stars, routine repo