https://github.com/bioai-oslo/vpc

Variational Place Cells

https://github.com/bioai-oslo/vpc

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Variational Place Cells

Basic Info
  • Host: GitHub
  • Owner: bioAI-Oslo
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 103 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 3 years ago · Last pushed 11 months ago
Metadata Files
Readme

README.md

Code for reproducing results in Decoding the Cognitive map: Learning place cells and remapping, published in eLife (2024).

To train models and generate results, first generate a dataset, using the create_dataset.ipynb notebook. The dataset contains 500-timestep trajectories visiting six distinct geometries. Network inputs consist of velocities along such trajectories, alongside a time-constant context signal unique to each environment. Labels contain Cartesian coordinates along trajectories. For non-path integrating models, uniformly sampled datasets may also be created, where both labels and inputs are Cartesian coordinates.

Then, create an experiment (i.e., a model) by running model_setup.py. A model name and path can be passed as the first argument to this run. This creates a model directory, wherein a JSON file is created, which specifies model hyperparameters. Edit this file to change e.g. the number of recurrent units.

Finally, train a model by running train_rnn.ipynb (for trajectory data) to train a recurrent network. Subsequent analyses can be found in the notebooks directory. For example, running spatial_representations.ipynb allows for loading a model, running it on a test dataset, and computing ratemaps of unit responses.

Owner

  • Name: bioAI
  • Login: bioAI-Oslo
  • Kind: organization

GitHub Events

Total
  • Push event: 3
Last Year
  • Push event: 3